From 4156b4e208f6907cab752ffa0ed492bf59197648 Mon Sep 17 00:00:00 2001 From: ericakane-moj <162118362+ericakane-moj@users.noreply.github.com> Date: Tue, 27 Aug 2024 12:53:01 +0100 Subject: [PATCH 01/14] bias blog 2 creation --- docs/blog/posts/2024-08-15-sd.md | 94 ++++++++++++++++++ .../posts/img/bias_investigation_steps.png | Bin 0 -> 86466 bytes .../posts/img/bias_mitigation_flowchart.png | Bin 0 -> 134400 bytes docs/blog/posts/img/match_weights_chart.png | Bin 0 -> 187032 bytes docs/blog/posts/img/sp_mistake_data.png | Bin 0 -> 151241 bytes 5 files changed, 94 insertions(+) create mode 100644 docs/blog/posts/2024-08-15-sd.md create mode 100644 docs/blog/posts/img/bias_investigation_steps.png create mode 100644 docs/blog/posts/img/bias_mitigation_flowchart.png create mode 100644 docs/blog/posts/img/match_weights_chart.png create mode 100644 docs/blog/posts/img/sp_mistake_data.png diff --git a/docs/blog/posts/2024-08-15-sd.md b/docs/blog/posts/2024-08-15-sd.md new file mode 100644 index 0000000000..48ed87307b --- /dev/null +++ b/docs/blog/posts/2024-08-15-sd.md @@ -0,0 +1,94 @@ +--- +date: 2024-08-23 +authors: + - erica-k +categories: + - Bias +--- + +# Bias in Data Linking #2 + +This Splink Blog is the second installment dedicated to the topic of bias. It wraps up the work completed during the the six-month [Alan Turing Institute](https://www.turing.ac.uk) internship on '_Bias in Data Linking_', summarising the final thoughts. + + + +## ⏪ Recap + +This blog builds upon the [previous post in this series](https://moj-analytical-services.github.io/splink/blog/2024/08/19/bias-in-data-linking.html)... + +In the first few months of the internship, the focus was on exploring potential sources of bias in data linking pipelines. This highlighted the complexity of these biases and the need for a standardised approach to evaluate them. As a result, a goal was set to develop such an approach. + +To achieve this, a common performance evaluation method using clerical review was examined to see how it could be adapted for bias assessment. However, challenges arose with this method, primarily due to difficulties in using real data to detect bias in the messy linkage output. + +## ⏩ Recent developments + +Building upon these challenges, it was determined that the output of a linkage pipeline (linked data) shouldn’t be used to _detect_ bias within a pipeline. Instead, it can be used to understand the _impact_ of any detected bias. + +Bias detection, which needs to come before assessing its impact, should be a key part of developing any linkage pipeline. This means using a bias hypothesis to shape how the model is trained and interpreted. + +This blog will explain a method for detecting bias in a data linking pipeline, broken down into 5 key steps: + +![Image 1](./img/bias_investigation_steps.png) + +These steps will be broken down to show how this bias detection process can be used with any data linkage pipeline... + +## 1. Generate synthetic data + +This process aims to build a neutral base for identification by creating a use-case specific dataset. While the process of generating this data can vary, it's crucial to keep it controlled. Automatically generating large amounts of data without understanding the content can lead to issues similar to using real production data. + +Instead of focussing on the size of the synthetic dataset, the key is ensuring each row has a clear purpose that is relevant to the hypothesis. Additionally, including records that capture potential downstream effects is useful, as they provide important context for the next steps. + +For example, if the hypothesis is that misspellings of names lead to worse linkage, and you suspect that misspellings are more common for names of certain origins, your synthetic data might look like this: + +![Image 2](./img/sp_mistake_data.png) + +This is a simplification, and a real generation would likely include more rows with various spelling error iterations. This data accounts for potential downstream effects by including similar names that relate to different individuals. + +## 2. Train and investigate model + +The model should then be trained with real production data and settings. After training, it can be analysed specifically in relation to the hypothesis, rather than just generally. In Splink, one way to do this is by using a [match weights chart](https://moj-analytical-services.github.io/splink/charts/match_weights_chart.html): + +![Image 3](./img/match_weights_chart.png) + +These model parameters will be relevant to the hypothesis, and it's useful to start thinking about how they relate to the scenarios in the synthetic data. However, because linkage relies on the accumulation of these weights, it’s difficult to fully understand their impact without generating comparisons. + +## 3. Perform and evaluate linkage + +Instead of dumping the entire dataset into the model, it’s better to manually decide which records be compared. This lets you focus on the scenarios you care about, rather than sifting through every possible combination. You'll generate match probabilities for each comparison and compare them to the pipeline's thresholds to see which records link. + +It’s also useful to examine each comparison to see which features impact the match probability the most. In Splink, you can use a [waterfall chart](https://moj-analytical-services.github.io/splink/charts/waterfall_chart.html) for this. This will help you identify if any weights are too predictive or not predictive enough based on your hypothesis. + +Some factor weightings might seem off for your hypothesis but be reasonable for the [overall model](#2-train-and-investigate-model). They might not be _wrong_ per se, but if they create issues in specific scenarios, they will **introduce bias into the pipeline**. + +## 4. Bias mitigation + +If you detect bias in the pipeline, you'll need to decide whether or not to address it. Here's some considerations which should help you make that decision: + +![Image 4](./img/bias_mitigation_flowchart.png) + +If you choose to attempt a bias mitigation, **repeat steps 1-3**, incorporating the technical solution before reassessing the results to determine if it was successful. Keep in mind, this attempt may only work partially - or not at all. + +This outcome will guide your next steps... + +## 5. Bias statement + +The mitigation stage could lead to three potential outcomes, each shaping a different statement about the bias in the pipeline: + +1. **Bias was not detected** +_You might disprove the hypothesis, though it's rare that there's no bias at all. Before concluding there's no bias in the pipeline, consider exploring other potential bias hypotheses._ + +2. **Bias was detected - it has been fully mitigated** +_It's unlikely, but possible. If this happens, ensure the impact on overall performance and any further bias is thoroughly investigated and clearly explained._ + +3. **Bias was detected - it has been partially/could not be mitigated** +_This is the most likely scenario. If mitigation is partial, clearly explain where it falls short. If there's no mitigation, work to gain a more detailed understanding of the bias._ + +If you conclude that bias has been detected and it can only be partially mitigated or not mitigated, this is not a failure. It's a likely outcome when you're trying to standardise a pipeline that handles thousands of different scenarios with varying impacts on results. + +## 💭 Final thoughts + +This approach doesn’t aim to make statements about the _impact_ of the detected bias. In other words, detecting bias alone doesn’t allow you to draw conclusions on how the resulting linked data will look. This is because many other unrelated factors in the pipeline will interact with the isolated scenarios and affect whether records are linked or not. + +Therefore, the goal here is to better understand bias. This is done by highlighting specific scenarios, examining how the model handles them, and introducing mitigations directly. This approach allows for more transparent statements about bias during the development of a linkage pipeline. + +We’ve created a simple demonstration of this process using Splink — please check it out and let us know your feedback! \ No newline at end of file diff --git a/docs/blog/posts/img/bias_investigation_steps.png b/docs/blog/posts/img/bias_investigation_steps.png new file mode 100644 index 0000000000000000000000000000000000000000..ef718e5dba9a608bc98b49e14eee542c3f840943 GIT binary patch literal 86466 zcmdSAWmuHo);K)0(g=t&h;$6yozf_sBOu+~B?1Bhigb75(9H}WA>G|AB|{JWAAcvG z=RD`j`|X|Uy629)_S!3UthM&qks4|WIGALZ0000-@wMz*000#N03ZcFK}USaFDv&1 z0I@5I**O7_371Q+vFHodgy_K8|*!DXMaNC7GW?YY^ zx>@~hM+xqInk5?_YK#1ZbtJhr9F?>0RWYZcaWM9as)-QNxNJsK1qB6w9{K__*-HTM z(g`d)Q2#Lh2#QXUOzFb_M1~8he(4(xg98BG#GseM7{N@t>_uD3TQ78S=)#bs+caW% z5}K4^ClkEmx){WhP+YJ8?>(s0(~to7+*_$=ci1IT$6<5&Z1|B#WACI7)6+#10WK`v z4P)}i)kfKgm!WFCtGtuwd_HZTr7AL_^S+?S`NUJK)IIW0KDKV{{6zQH`%#v~j1!YM zO1g8Tb6Malq!ZDkknx-pt(w1^S{6A>tec5X^LbiMok9%Uha!b12cx<+nB)a+H9>dh zM#j%4#Mwz4m^;$gx=)0)6Gmeq^r$z&y4rFC6NlyZ3iJ|zb>hYYrsZ%q(b6vP(kzO; zC$eGTqp;>r?LG7Jt|9oVPo?;V&p$n*qb-s-Oh~3A`Cdb+ozfpkQT27eZ2QDenu3)m z0+s2a`h&ZTbHxDUm4MSb$Xu@qfxz3at8J^^2$r+ZdYDfXeBG#OP?}4`R8k>DfWhn? z2eRqAFRs7!k~K2$izlvW`td$#$M%%QXILkSIdTj={d%^8-?~HGUD~@5dF?F4a&h9W z80g-{t=7gFP4c|*GX@C#B>)2znc+)k8ahJ=8;$LO&94xzfvk5OC~vP3cj0_((&;?-HTd5?)hX7Bib;+$8}w6SK(3;{l!io z8uIxU^@>l`scD-~)xhnem_$dQ)!dgz4XzrbIF%SAWix!PFE5m^85Y*Vn}EM#6gtW! 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z48hn$TQ%@0fq(9eQR5rAq!~hNszw;LTl@;=*ud`(H;;Gz7EVaf=X;NSkGYU$SsM7e z%>VYy)OvmOdLBh*^$&}mlkhgnwFhTPf15s{4bAWxZ}vS{acfg5*1%9ngxb=JxoZ&% zxRr%|cctm!GYkL7_ovjT2P|)OOEvyL5Xrd>PyZReT)-EecKGU{wYbB*O>J0xG3U;l zq{IgoHZjz{p}9$emu4`OLs?SsW8l1H>M#4Vn}YbnpH${PC0CuVhV=I|SGxb7d)DQ? z4OlotK1f&s_(=LkOAL6i+?Yjrlept1!rpzqHRs2fp*-r+W9Mlc56@UIbjkZHnS!wPcBjxF?saW*yjoj6Jy;2rMM=dR>W>VNA^39|h#T7F6Gzx8b2mT&`n z1gG)AuN9SJyr3=--)ODrBS%=vHqVK|61X?>?SRNSHiRk zYj3RVza;v%a@+>ICI3iw!(IKafB9Ml>~cbilHdRRE587ebUexWzdq{Nb4OtHA~KMf z|Mi;c+O6W+~i+TG@t{LjCf zNdR1lZ%){&jX3qcKI+Ud>6gz%%)8geZT&yKL8J(fAP_ZI4gTAM{^v8#@X4ZuuQ#%Y zjYEY0xhgok_Uk|qFJ^uAz8&3P*5V>I<|AxrTT;<;!Y{NW`{hNbrXvM!d*k%U% sZ<4=}asQj-Z(56p{68UO$Q From 35699b38fbc7e7fba8339813b0c3fb83c3abbbf8 Mon Sep 17 00:00:00 2001 From: ericakane-moj <162118362+ericakane-moj@users.noreply.github.com> Date: Thu, 29 Aug 2024 10:44:06 +0100 Subject: [PATCH 03/14] sp updates --- docs/blog/posts/2024-08-15-sd.md | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/docs/blog/posts/2024-08-15-sd.md b/docs/blog/posts/2024-08-15-sd.md index bec8e02462..040a99a1bc 100644 --- a/docs/blog/posts/2024-08-15-sd.md +++ b/docs/blog/posts/2024-08-15-sd.md @@ -60,9 +60,11 @@ It’s also useful to examine each comparison to see which features impact the m Some factor weightings might seem off for your hypothesis but be reasonable for the [overall model](#2-train-and-investigate-model). They might not be _wrong_ per se, but if they create issues in specific scenarios, they will **introduce bias into the pipeline**. -## 4. Bias mitigation +## 4. Identify bias mitigations -If you detect bias in the pipeline, you'll need to decide whether or not to address it. Here's some considerations which should help you make that decision: +If you detect bias in the pipeline, you'll need to decide whether or not to address it. Assessing the mitigation strategy in this controlled manner ensures it directly addresses the bias. Focus on aligning the mitigation with the hypothesis, rather finding a solution than randomly pushes comparisons over a threshold. + +Here's some considerations which should help you make that decision: ![Image 4](./img/bias_mitigation_flowchart.png) @@ -70,12 +72,12 @@ If you choose to attempt a bias mitigation, **repeat steps 1-3**, incorporating This outcome will guide your next steps... -## 5. Bias statement +## 5. Make a statement about bias The mitigation stage could lead to three potential outcomes, each shaping a different statement about the bias in the pipeline: 1. **Bias was not detected** -_You might disprove the hypothesis, though it's rare that there's no bias at all. Before concluding there's no bias in the pipeline, consider exploring other potential bias hypotheses._ +_You might disprove the hypothesis, though it's rare that there's no bias at all. Before concluding there's no bias in the pipeline, consider exploring other hypotheses._ 2. **Bias was detected - it has been fully mitigated** _It's unlikely, but possible. If this happens, ensure the impact on overall performance and any further bias is thoroughly investigated and clearly explained._ @@ -87,7 +89,7 @@ If you conclude that bias has been detected and it can only be partially mitigat ## 💭 Final thoughts -This approach doesn’t aim to make statements about the _impact_ of the detected bias. In other words, detecting bias alone doesn’t allow you to draw conclusions on how the resulting linked data will look. This is because many other unrelated factors in the pipeline will interact with the isolated scenarios and affect whether records are linked or not. +This approach doesn’t aim to make statements about the _impact_ of the detected bias. In other words, detecting bias alone doesn’t allow you to draw conclusions on how the resulting linked data will look. This is because many other unrelated factors in the pipeline will interact with the scenarios and affect whether records are linked or not. Therefore, the goal here is to better understand bias. This is done by highlighting specific scenarios, examining how the model handles them, and introducing mitigations directly. This approach allows for more transparent statements about bias during the development of a linkage pipeline. From 953641ec4ef67832229f61f4d7e74fa566d39c20 Mon Sep 17 00:00:00 2001 From: ericakane-moj <162118362+ericakane-moj@users.noreply.github.com> Date: Thu, 29 Aug 2024 10:51:18 +0100 Subject: [PATCH 04/14] sp --- docs/blog/posts/2024-08-15-sd.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/blog/posts/2024-08-15-sd.md b/docs/blog/posts/2024-08-15-sd.md index 040a99a1bc..8cde188739 100644 --- a/docs/blog/posts/2024-08-15-sd.md +++ b/docs/blog/posts/2024-08-15-sd.md @@ -42,7 +42,7 @@ For example, if the hypothesis is that misspellings of names lead to worse linka ![Image 2](./img/sp_mistake_data.png) -This is a simplification, and a real generation would likely include more rows with various spelling error iterations. This data accounts for potential downstream effects by including similar names that relate to different individuals. +This is a simplification, and a real generation would likely include more rows with various spelling error iterations. This data accounts for potential downstream effects by including similarly spelt names that relate to different individuals. ## 2. Train and investigate model From b8f99ec2e1d3a0bcb6dda8389bb86e84cd5da7d8 Mon Sep 17 00:00:00 2001 From: ericakane-moj <162118362+ericakane-moj@users.noreply.github.com> Date: Thu, 29 Aug 2024 14:54:39 +0100 Subject: [PATCH 05/14] rm model training --- docs/blog/posts/2024-08-15-sd.md | 24 +++++++++--------- .../posts/img/bias_investigation_steps.png | Bin 86466 -> 158396 bytes 2 files changed, 12 insertions(+), 12 deletions(-) diff --git a/docs/blog/posts/2024-08-15-sd.md b/docs/blog/posts/2024-08-15-sd.md index 8cde188739..57ae7cbdd6 100644 --- a/docs/blog/posts/2024-08-15-sd.md +++ b/docs/blog/posts/2024-08-15-sd.md @@ -24,37 +24,37 @@ To achieve this, a common performance evaluation method using clerical review wa Building upon these challenges, it was determined that the output of a linkage pipeline (linked data) shouldn’t be used to _detect_ bias within a pipeline. Instead, it can be used to understand the _impact_ of any detected bias. -Bias detection, which needs to come before assessing its impact, should be a key part of developing any linkage pipeline. This means using a bias hypothesis to shape how the model is trained and interpreted. +Bias detection, which needs to come before assessing its impact, should be a key part of understanding any linkage pipeline. This means using a bias hypothesis to shape how the model is interpreted. Therefore, this blog will explain an approach for detecting bias in an existing data linking pipeline which has a pre-trained model. There is also a [supplementary notebook]() which demonstrates how this process can work practically using Splink. -This blog will explain a method for detecting bias in a data linking pipeline, broken down into 5 key steps: +The approach is broken down into 5 key steps: ![Image 1](./img/bias_investigation_steps.png) -These steps will be broken down to show how this bias detection process can be used with any data linkage pipeline... +These steps will be explained to show how this bias detection process can be used with any data linkage pipeline... -## 1. Generate synthetic data +## 1. Generate synthetic records -This process aims to build a neutral base for identification by creating a use-case specific dataset. While the process of generating this data can vary, it's crucial to keep it controlled. Automatically generating large amounts of data without understanding the content can lead to issues similar to using real production data. +This process builds a neutral foundation for bias detection by creating specific records to evaluate the pre-trained model's results. While the process of generating these records can vary, it's crucial to keep it controlled. Automatically generating large amounts of data without understanding the content can lead to issues similar to using real production data. -Instead of focussing on the size of the synthetic dataset, the key is ensuring each row has a clear purpose that is relevant to the hypothesis. Additionally, including records that capture potential downstream effects is useful, as they provide important context for the next steps. +Instead of focussing on the number of synthetic records, the key is ensuring each has a clear purpose that is relevant to the hypothesis. Additionally, including records that capture potential downstream effects is useful, as they provide important context for the next steps. -For example, if the hypothesis is that misspellings of names lead to worse linkage, and you suspect that misspellings are more common for names of certain origins, your synthetic data might look like this: +For example, if the hypothesis is that misspellings of names lead to worse linkage, and you suspect that misspellings are more common for names of certain origins, your synthetic records might look like this: ![Image 2](./img/sp_mistake_data.png) -This is a simplification, and a real generation would likely include more rows with various spelling error iterations. This data accounts for potential downstream effects by including similarly spelt names that relate to different individuals. +This is a simplification, and a real generation would likely include more rows with various spelling error iterations. The last record also accounts for potential downstream effects by including similarly spelt names that relate to different individuals. -## 2. Train and investigate model +## 2. Investigate model parameters -The model should then be trained with real production data and settings. After training, it can be analysed specifically in relation to the hypothesis, rather than just generally. In Splink, one way to do this is by using a [match weights chart](https://moj-analytical-services.github.io/splink/charts/match_weights_chart.html): +The pre-trained model can be analysed specifically in relation to the hypothesis, rather than just generally. In Splink, one way to do this is by using a [match weights chart](https://moj-analytical-services.github.io/splink/charts/match_weights_chart.html): ![Image 3](./img/match_weights_chart.png) -These model parameters will be relevant to the hypothesis, and it's useful to start thinking about how they relate to the scenarios in the synthetic data. However, because linkage relies on the accumulation of these weights, it’s difficult to fully understand their impact without generating comparisons. +These model parameters will be relevant to the hypothesis, and it's useful to start thinking about how they relate to the scenario represented by the synthetic records. However, because linkage relies on the accumulation of these weights, it’s difficult to fully understand their impact without generating comparisons. ## 3. Perform and evaluate linkage -Instead of dumping the entire dataset into the model, it’s better to manually decide which records be compared. This lets you focus on the scenarios you care about, rather than sifting through every possible combination. You'll generate match probabilities for each comparison and compare them to the pipeline's thresholds to see which records link. +Instead of dumping the every record into the model, it’s better to manually decide which to compare. This lets you focus on the scenarios you care about, rather than sifting through every possible combination. You'll generate match probabilities for each comparison and compare them to the pipeline's thresholds to see which records link. It’s also useful to examine each comparison to see which features impact the match probability the most. In Splink, you can use a [waterfall chart](https://moj-analytical-services.github.io/splink/charts/waterfall_chart.html) for this. 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"0 1 1 sarah brown 1862-07-11 london \n", + "\n", + " postcode_fake gender occupation \n", + "0 ba12 0ay female politician " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "synthetic_base_df" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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unique_idperson_idfirst_namesurnamedobbirth_placepostcode_fakegenderoccupation
021sarahbrown1862-07-11londonba12 0ayfemalepolitician
131sarahdoyle1862-07-11londonba12 0ayfemalepolitician
241sarahbrown1862-07-11londontf3 2ngfemalepolitician
351sarahdoyle1862-07-11londontf3 2ngfemalepolitician
462janebrown1860-01-01londonba12 0ayfemaleartist
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" + ], + "text/plain": [ + " unique_id person_id first_name surname dob birth_place \\\n", + "0 2 1 sarah brown 1862-07-11 london \n", + "1 3 1 sarah doyle 1862-07-11 london \n", + "2 4 1 sarah brown 1862-07-11 london \n", + "3 5 1 sarah doyle 1862-07-11 london \n", + "4 6 2 jane brown 1860-01-01 london \n", + "\n", + " postcode_fake gender occupation \n", + "0 ba12 0ay female politician \n", + "1 ba12 0ay female politician \n", + "2 tf3 2ng female politician \n", + "3 tf3 2ng female politician \n", + "4 ba12 0ay female artist " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "synthetic_comparison_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The first row is the base individual, with each subsequent row representing a different comparison related to the hypothesis. The first comparison is a self-link to serve as a baseline. Next, rows represent isolated surname changes and postcode changes, and finally a combination of both. \n", + "\n", + "Since this hypothesis relates to the linkage of records that _look different but are for the same person_, it’s helpful to include a record that _looks similar but is for a different person_ (like a sibling or partner). This will help account for the downstream effects of any mitigation option (Step 4).\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### **2. Train and investigate model**\n", + "\n", + "Train the model on the real production data (this Splink model is trained in 'model_create_h50k.ipynb' and saved as a json file). \n", + "You can refer to the [Splink tutorial](https://moj-analytical-services.github.io/splink/demos/tutorials/00_Tutorial_Introduction.html) if you need any further explanation on how to prepare the data and define the model settings. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "from splink import DuckDBAPI\n", + "from splink import Linker, SettingsCreator\n", + "from splink import splink_datasets\n", + "\n", + "db_api = DuckDBAPI()\n", + "\n", + "production_df = splink_datasets.historical_50k" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "linker = Linker(production_df, settings='model_h50k.json', db_api=db_api)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It's useful to visualise the model parameters to learn the relative importance of different parts of your data for linking.\n", + "\n", + "You can do this in Splink by using a match weights chart:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.VConcatChart(...)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "linker.visualisations.match_weights_chart()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When developing this model, it's essential to evaluate the parameter weights to ensure they align with the pipeline's goals. However, this approach requires considering the bias hypothesis.\n", + "\n", + "At first glance, the model appears normal, but a few key points stand out regarding the hypothesis (focussed on surname and postcode changes):\n", + "\n", + "- A non-match on the surname is more predictive than a non-match on the first name.\n", + "- A postcode match has the highest predictive weight.\n", + "\n", + "The cumulative effect of these weights makes it difficult to fully understand their impact without generating comparisons.\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### **3. Perform and evaluate linkage**\n", + "\n", + "In a standard linkage with Splink, all records which meet the criteria of the blocking rules would be compared against each other. However, in bias detection we may not be interested in a lot of the comparisons this process will generate match probabilities for. It can be easier to manually generate the comparisons that are relevant to the hypothesis. " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def compare_records(base_records, comparison_records, linker):\n", + " results = []\n", + " for record_1 in base_records:\n", + " results.extend(\n", + " linker.inference.compare_two_records(record_1, record_2).as_pandas_dataframe()\n", + " for record_2 in comparison_records\n", + " )\n", + " all_comparisons_df = pd.concat(results, ignore_index=True)\n", + " return all_comparisons_df" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No term frequencies found for column \"surname\".\n", + "To apply term frequency adjustments, you need to register a lookup using `linker.table_management.register_term_frequency_lookup`.\n", + "No term frequencies found for column \"first_name\".\n", + "To apply term frequency adjustments, you need to register a lookup using `linker.table_management.register_term_frequency_lookup`.\n", + "No term frequencies found for column \"surname\".\n", + "To apply term frequency adjustments, you need to register a lookup using `linker.table_management.register_term_frequency_lookup`.\n", + "No term frequencies found for column \"first_name\".\n", + "To apply term frequency adjustments, you need to register a lookup using `linker.table_management.register_term_frequency_lookup`.\n", + "No term frequencies found for column \"surname\".\n", + "To apply term frequency adjustments, you need to register a lookup using `linker.table_management.register_term_frequency_lookup`.\n", + "No term frequencies found for column \"first_name\".\n", + "To apply term frequency adjustments, you need to register a lookup using `linker.table_management.register_term_frequency_lookup`.\n", + "No term frequencies found for column \"surname\".\n", + "To apply term frequency adjustments, you need to register a lookup using `linker.table_management.register_term_frequency_lookup`.\n", + "No term frequencies found for column \"first_name\".\n", + "To apply term frequency adjustments, you need to register a lookup using `linker.table_management.register_term_frequency_lookup`.\n", + "No term frequencies found for column \"surname\".\n", + "To apply term frequency adjustments, you need to register a lookup using `linker.table_management.register_term_frequency_lookup`.\n", + "No term frequencies found for column \"first_name\".\n", + "To apply term frequency adjustments, you need to register a lookup using `linker.table_management.register_term_frequency_lookup`.\n" + ] + } + ], + "source": [ + "comparisons = compare_records(synthetic_base_raw, synthetic_comparison_raw, linker)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Look at the resulting match probabilities in terms of the threshold of your pipeline. \n", + "This pipeline has a high match threshold of 0.999, medium of 0.99, and low of 0.95." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "def highlight_cells(val):\n", + " if val >= 0.999:\n", + " color = '#c4f5bf' # High threshold, green\n", + " elif val >= 0.99:\n", + " color = '#faf9c0' # Medium threshold, yellow\n", + " elif val >= 0.95:\n", + " color = '#f5e1bf' # Low threshold, orange\n", + " else:\n", + " color = '#f5c8bf' # Below threshold, red\n", + " return f'background-color: {color}'" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 match_weightmatch_probabilityunique_id_lunique_id_rfirst_name_lfirst_name_rsurname_lsurname_rdob_ldob_rpostcode_fake_lpostcode_fake_rbirth_place_lbirth_place_roccupation_loccupation_r
035.0457941.00000012sarahsarahbrownbrown1862-07-111862-07-11ba12 0ayba12 0aylondonlondonpoliticianpolitician
121.1302631.00000013sarahsarahbrowndoyle1862-07-111862-07-11ba12 0ayba12 0aylondonlondonpoliticianpolitician
219.4522600.99999914sarahsarahbrownbrown1862-07-111862-07-11ba12 0aytf3 2nglondonlondonpoliticianpolitician
35.5367280.97891315sarahsarahbrowndoyle1862-07-111862-07-11ba12 0aytf3 2nglondonlondonpoliticianpolitician
48.0468010.99623316sarahjanebrownbrown1862-07-111860-01-01ba12 0ayba12 0aylondonlondonpoliticianartist
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "columns_of_interest = ['match_weight', 'match_probability', 'unique_id_l', 'unique_id_r', 'first_name_l', 'first_name_r', 'surname_l','surname_r', 'dob_l', 'dob_r','postcode_fake_l', 'postcode_fake_r','birth_place_l', 'birth_place_r', 'occupation_l', 'occupation_r']\n", + "comparisons[columns_of_interest].style.map(highlight_cells, subset=['match_probability'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The first three comparisons show high match probabilities. A surname change or postcode change alone allow for a linkage at a high match threshold. \n", + "However, when combined they lower the match probability to 0.9781 - which could only be linked at a low threshold in the example pipeline. \n", + "\n", + "Additionally, it is useful to note that the sibling scenario has a a higher match probability than the surname and postcode change scenario, and it can be matched at a medium threshold. \n", + "\n", + "It’s helpful to break these results down further to understand how the individual model parameters are combining to result in these final probabilities. This can be done in Splink using the waterfall chart:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.LayerChart(...)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "records_to_plot = comparisons.to_dict(orient=\"records\")\n", + "linker.visualisations.waterfall_chart(records_to_plot)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Taking a closer look at each comparison helps reveal which features have the most impact in terms of the hypothesis. \n", + "\n", + "Within the model, matching on postcode and surname are the strongest indicators of a link. Therefore, when both surname and postcode change, you lose that strong link advantage. So, it's not that a non-match on surname or postcode is overly detrimental to the records being linked, but without the boost from a surname or postcode match, they only meet the low linkage threshold. In contrast, in the sibling scenario, you can see the impact of one or two strong predictors in a model can have. Even with three non-matches (first name, DOB, and occupation), matching on both postcode and surname links the records at a medium threshold. Also, first name matches are less predictive than surname matches - so the sibling scenario has a more predictive name match than changing information. \n", + "\n", + "Due to the way individual factors are weighted, the combination of non-matches in this scenario means that people who change both their postcode and surname won't be linked. Since this affects women more than men in our data, it leads to a **gender bias** in the pipeline.\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### **4. Identify mitigations**\n", + "\n", + "Now that bias has been detected, it's time to explore potential mitigation strategies:\n", + "\n", + "Are there viable technical solutions?\n", + "\n", + "In our example, a change in surname and postcode allows records to be linked at a low threshold. We should consider what can be done in the pipeline to ensure these comparisons are linked.\n", + "\n", + "Any technical solution should align with the hypothesis, as opposed to just pushing comparisons randomly over/under a threshold. Different elements of the pipeline can be altered to address the bias. \n", + "\n", + "- Altering the _input data_ - there's likely no potential solution here, as the bias stems from legitimate data qualities, not errors. \n", + "- Updating the _model design_ - a TF adjustment might alter results, but this would be random so wouldn't address the hypothesis. However, **adjusting comparison levels** might help. \n", + "- Adjusting the _output data_ - since the records link at a low threshold, **lowering the threshold** across the model would result in a link.\n", + "\n", + "Could they negatively impact overall performance?\n", + "\n", + "**Lowering the threshold**\n", + "\n", + "This could link records with surname and postcode changes but might also increase false positives, impacting overall performance. This trade-off needs careful consideration. In synthetic data, for instance, siblings would link at low thresholds. This is just one scenario, and there are most likely others. Given these potential issues, we’re not pursuing a lower threshold.\n", + "\n", + "**Adjusting comparison levels**\n", + "\n", + "We'd need to decide that a factor’s predictive power doesn't fit our hypothesis - but also make sure this applies across the whole model. There are lots of options, like making surname or postcode less predictive, or boosting factors like occupation, first name, DOB, or birthplace. We'll focus on one example to demonstrate the necessary considerations. \n", + "\n", + "Say we decide to make first name as predictive as surname, and we find that this change won’t impact overall performance. Therefore, we can take this technical solution to the next consideration.\n", + "\n", + "Could they introduce further bias?\n", + "\n", + "It's important to think about other groups that this decision could impact. \n", + "\n", + "In this dataset, there's records relating to a community of Vietnamese people. They share similar postcodes and often list the same birthplace (because the dataset defaults to the country of birth if they were born outside the UK). Many families in this community have the same surname, as do many unrelated individuals (due to less variation in Vietnamese surnames). Additionally, because Vietnamese naming conventions differ from Western ones, surnames are often incorrectly recorded as first names in this dataset. \n", + "\n", + "Therefore, increasing the predictive power of first name matches will likely lead to more false positives for this community, introducing bias against the Vietnamese population in the dataset. The decision is made to not attempt bias mitigation.\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### **5. Make a statement about bias** \n", + "\n", + "When a bias is detected but not mitigated, it’s crucial to explore it further to gain more insight. This helps refine the understanding of the bias and lays the groundwork for impact assessment.\n", + "\n", + "In our example, we can dig deeper into how the link fails with postcode and surname changes. To do this, we’ll create some more synthetic data with partial changes in surname and postcode:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "synthetic_comparison_partial_raw = [\n", + " {\"unique_id\": 2, \"person_id\": 1, \"first_name\": \"sarah\", \"surname\": \"brown-doyle\", \"dob\": \"1862-07-11\", \"birth_place\": \"london\", \"postcode_fake\": \"tf3 2ng\", \"gender\": \"female\", \"occupation\": \"politician\"},\n", + " {\"unique_id\": 3, \"person_id\": 1, \"first_name\": \"sarah\", \"surname\": \"doyle\", \"dob\": \"1862-07-11\", \"birth_place\": \"london\", \"postcode_fake\": \"ba13 2ng\", \"gender\": \"female\", \"occupation\": \"politician\"},\n", + " {\"unique_id\": 4, \"person_id\": 1, \"first_name\": \"sarah\", \"surname\": \"brown-doyle\", \"dob\": \"1862-07-11\", \"birth_place\": \"london\", \"postcode_fake\": \"ba13 2ng\", \"gender\": \"female\", \"occupation\": \"politician\"}\n", + "]\n", + "\n", + "synthetic_comparison_partial_df = pd.DataFrame(synthetic_comparison_partial_raw)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this scenario, we generate new records to be compared to the base. These represent a partial surname change (double-barrel) and a full postcode change, a partial postcode change (same area, different district) with a full surname change, and a combination of partial postcode and partial surname changes.\n", + "\n", + "We can then use the same model to link these records and examine the resulting match probabilities. " + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No term frequencies found for column \"surname\".\n", + "To apply term frequency adjustments, you need to register a lookup using `linker.table_management.register_term_frequency_lookup`.\n", + "No term frequencies found for column \"first_name\".\n", + "To apply term frequency adjustments, you need to register a lookup using `linker.table_management.register_term_frequency_lookup`.\n", + "No term frequencies found for column \"surname\".\n", + "To apply term frequency adjustments, you need to register a lookup using `linker.table_management.register_term_frequency_lookup`.\n", + "No term frequencies found for column \"first_name\".\n", + "To apply term frequency adjustments, you need to register a lookup using `linker.table_management.register_term_frequency_lookup`.\n", + "No term frequencies found for column \"surname\".\n", + "To apply term frequency adjustments, you need to register a lookup using `linker.table_management.register_term_frequency_lookup`.\n", + "No term frequencies found for column \"first_name\".\n", + "To apply term frequency adjustments, you need to register a lookup using `linker.table_management.register_term_frequency_lookup`.\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 match_weightmatch_probabilityunique_id_lunique_id_rfirst_name_lfirst_name_rsurname_lsurname_rdob_ldob_rpostcode_fake_lpostcode_fake_rbirth_place_lbirth_place_roccupation_loccupation_r
015.6417000.99998012sarahsarahbrownbrown-doyle1862-07-111862-07-11ba12 0aytf3 2nglondonlondonpoliticianpolitician
112.1307170.99977713sarahsarahbrowndoyle1862-07-111862-07-11ba12 0ayba13 2nglondonlondonpoliticianpolitician
222.2356891.00000014sarahsarahbrownbrown-doyle1862-07-111862-07-11ba12 0ayba13 2nglondonlondonpoliticianpolitician
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "comparisons_partial = compare_records(synthetic_base_raw, synthetic_comparison_partial_raw, linker)\n", + "\n", + "comparisons_partial[columns_of_interest].style.map(highlight_cells, subset=['match_probability'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In all three cases, the records will remain linked at a high threshold. This insight can help make the final bias statement:\n", + "\n", + "_Given how the model weighs the different parameters to produce match probabilities, a full change in both surname and postcode will lead to a non-link. As women are more likely than men to make these changes in the input data, this will introduce a gender bias in the pipeline._\n", + "\n", + "While some other demographics may be likely to change either names or addresses, a link only fails when both changes occur together. In instances where individuals outside a specific demographic also make both changes, it’s not considered bias — just an impact on overall performance. If this change does affect a specific demographic, it doesn’t negate the bias against women, but adds another layer of bias.\n", + "\n", + "Now that this bias has been detected, it's important to note that we can't yet determine its exact impact on the linked data. This is because the final results are influenced by many other factors in the pipeline, which were intentionally excluded from this process to isolate the bias. \n", + "\n", + "Further investigation is needed to understand impact — such as understanding how many records in the input data undergo these changes, or how many records with a full surname and postcode change end up linked or not linked in the final data. " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/demos/examples/examples_index.md b/docs/demos/examples/examples_index.md index ef2ed59d7f..4efee89555 100644 --- a/docs/demos/examples/examples_index.md +++ b/docs/demos/examples/examples_index.md @@ -52,6 +52,10 @@ This section provides a series of examples to help you get started with Splink. Open In Colab
[Cookbook of various Splink techniques](./duckdb/cookbook.ipynb) + + Open In Colab + [Investigating Bias in a Splink Model](./duckdb/bias_eval.ipynb) + ##### Entity type: Financial transactions diff --git a/mkdocs.yml b/mkdocs.yml index 3e8144097b..5a42085b2c 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -124,6 +124,7 @@ nav: - Febrl3 Dedupe: "demos/examples/duckdb/febrl3.ipynb" - Febrl4 link-only: "demos/examples/duckdb/febrl4.ipynb" - Cookbook: "demos/examples/duckdb/cookbook.ipynb" + - Investigating Bias in a Splink Model: "demos/examples/duckdb/bias_eval.ipynb" - PySpark: - Deduplication using Pyspark: "demos/examples/spark/deduplicate_1k_synthetic.ipynb" - Athena: From c1dc2994c54a9c899c02c277e60bb4979c120dab Mon Sep 17 00:00:00 2001 From: Ross Kennedy Date: Tue, 24 Sep 2024 16:00:17 +0100 Subject: [PATCH 08/14] Update blog wording --- ...-15-sd.md => 2024-08-15-bias-continued.md} | 48 ++++++++++--------- 1 file changed, 26 insertions(+), 22 deletions(-) rename docs/blog/posts/{2024-08-15-sd.md => 2024-08-15-bias-continued.md} (56%) diff --git a/docs/blog/posts/2024-08-15-sd.md b/docs/blog/posts/2024-08-15-bias-continued.md similarity index 56% rename from docs/blog/posts/2024-08-15-sd.md rename to docs/blog/posts/2024-08-15-bias-continued.md index 2085d2f5b8..ee64f322c6 100644 --- a/docs/blog/posts/2024-08-15-sd.md +++ b/docs/blog/posts/2024-08-15-bias-continued.md @@ -1,20 +1,24 @@ --- -date: 2024-08-27 +date: 2024-09-19 authors: - erica-k categories: - Bias --- -# Bias in Data Linking #2 +# Bias in Data Linking, continued This Splink Blog is the second installment dedicated to the topic of bias. It wraps up the work completed during the the six-month [Alan Turing Institute](https://www.turing.ac.uk) internship on '_Bias in Data Linking_', summarising the final thoughts. +!!! note + + This blog is intentionally high-level, with specific Splink examples in code provided in the [accompanying notebook](../../demos/examples/duckdb/bias_eval.ipynb). + ## ⏪ Recap -This blog builds upon the [previous post in this series](https://moj-analytical-services.github.io/splink/blog/2024/08/19/bias-in-data-linking.html)... +This blog builds upon the [previous post in this series](https://moj-analytical-services.github.io/splink/blog/2024/08/19/bias-in-data-linking.html). In the first few months of the internship, the focus was on exploring potential sources of bias in data linking pipelines. This highlighted the complexity of these biases and the need for a standardised approach to evaluate them. As a result, a goal was set to develop such an approach. @@ -22,25 +26,21 @@ To achieve this, a common performance evaluation method using clerical review wa ## ⏩ Recent developments -Building upon these challenges, it was determined that the output of a linkage pipeline (linked data) shouldn’t be used to _detect_ bias within a pipeline. Instead, it can be used to understand the _impact_ of any detected bias. +Building upon these challenges, it was determined that the output of a linkage pipeline (i.e. the linked data) shouldn’t be used to _detect_ bias within a pipeline. Instead, it can be used to understand the _impact_ of any detected bias. -Detecting bias should be guided by a hypothesis to form a key part of understanding any data linkage pipeline. This blog proposes an approach to bias detection, broken down into 5 key steps: +Detecting bias should be guided by a hypothesis to form a key part of understanding any data linkage pipeline. This blog proposes a high-level approach to bias detection, broken down into 5 key steps: ![Image 1](./img/bias_investigation_steps.png) -These steps will be explained to show how you can apply this bias detection process to any data linkage pipeline. There's also a [supplementary notebook]() that demonstrates how to do this in practice using Splink. - ## 1. Generate synthetic records -This process builds a neutral foundation for bias detection by creating specific records to evaluate a pre-trained model's results. While the process of generating these records can vary, it's crucial to keep it controlled. Automatically generating large amounts of data without understanding the content can lead to issues similar to using real production data. +This process builds a neutral foundation for bias detection by creating specific records to evaluate a pre-trained model's results. While the process of generating these records can vary, it's crucial to keep it controlled. Automatically generating large amounts of data without understanding the content can lead to the same issues as real production data. -Instead of focussing on the number of synthetic records, the key is ensuring each has a clear purpose that is relevant to the hypothesis. Additionally, including records that capture potential downstream effects is useful, as they provide important context for the next steps. - -For example, if the hypothesis is that misspellings of names lead to worse linkage, and you suspect that misspellings are more common for names of certain origins, your synthetic records might look like this: +Instead of focussing on the number of synthetic records, the key is ensuring each record has a clear purpose that is relevant to the hypothesis. For example, if the hypothesis is that misspellings of names lead to worse linkage, and you suspect that misspellings are more common for names of certain origins, your synthetic records might look like this: ![Image 2](./img/sp_mistake_data.png) -This is a simplification, and a real generation would likely include more rows with various spelling error iterations. The last record also accounts for potential downstream effects by including similarly spelt names that relate to different individuals. +This is a simplification, and a real generation would likely include more rows with various spelling error iterations. The final two record also consider the case where similarly spelt names that relate to different individuals. ## 2. Investigate model parameters @@ -48,11 +48,13 @@ The pre-trained model can be analysed specifically in relation to the hypothesis ![Image 3](./img/match_weights_chart.png) -These model parameters will be relevant to the hypothesis, and it's useful to start thinking about how they relate to the scenario represented by the synthetic records. However, because linkage relies on the accumulation of these weights, it’s difficult to fully understand their impact without generating comparisons. +Each of the bars represent the amount of evidence (match weights) that two records to refer to the same individual depending on how similar each of the feature are. It's useful to start thinking about how this chart relates to the scenario represented by the synthetic records. For example, for the above model there is only a significant drop in match weight for first_name between the third and fourth comparison level. + +Given the final linkage score relies on the accumulation of these match weights, viewing the model parameters in isolation like this does not give the full picture - specific examples are required. ## 3. Perform and evaluate linkage -Instead of dumping the every record into the model, it’s better to manually decide which to compare. This lets you focus on the scenarios you care about, rather than sifting through every possible combination. You'll generate match probabilities for the synthetic record comparisons using the pre-trained model, comparing results to the pipeline's threshold to see which records link. +Now that you have records to compare and a trained model, the next stage is to generate match probabilities. If a threshold for a link has been chosen, this can be used to indicate which records would be considered to be a link in a linkage pipeline. It’s also useful to examine each comparison to see which features impact the match probability the most. In Splink, you can use a [waterfall chart](https://moj-analytical-services.github.io/splink/charts/waterfall_chart.html) for this. This will help you identify if any weights are too predictive or not predictive enough based on your hypothesis. @@ -60,22 +62,24 @@ Some factor weightings might seem off for your hypothesis but be reasonable for ## 4. Identify bias mitigations -If you detect bias in the pipeline, you'll need to decide whether or not to address it. Assessing the mitigation strategy in this controlled manner ensures it directly addresses the bias. Focus on aligning the mitigation with the hypothesis, rather finding a solution than randomly pushes comparisons over a threshold. +If you detect bias in the pipeline, you'll need to decide whether or not to address it. Assessing the mitigation strategy in this controlled manner ensures it directly addresses the bias. -Here's some considerations which should help you make that decision: +Here's an example of how you could approach deciding whether to mitigate for bias or not: ![Image 4](./img/bias_mitigation_flowchart.png) -If you choose to attempt a bias mitigation, **repeat steps 1-3**, incorporating the technical solution before reassessing the results to determine if it was successful. Keep in mind, this attempt may only work partially - or not at all. +In reality there will often be some negative impact on overall performance and/or further biases introduced by changing the model, so the decision is not straightfoward. It depends on the specific use case. -This outcome will guide your next steps... +If you choose to attempt a bias mitigation, **repeat steps 1-3** before reassessing the results to determine if it was successful. Keep in mind, this attempt may only work partially - or not at all. ## 5. Make a statement about bias -The mitigation stage could lead to three potential outcomes, each shaping a different statement about the bias in the pipeline: +As discussed, investigating bias should be anchored in specific hypotheses and examples and the reporting of bias within a model should follow the same structure. It is unrealistic to expect that there will be no bias at all, or that it is practical to be able to check enough hypotheses to make such a broad statement. Instead, it is recommended to report on each of the hypotheses that have been tested. + +The mitigation stage can lead to three potential outcomes, each shaping a different statement about the the specific bias from your hypothesis: 1. **Bias was not detected** -_You might disprove the hypothesis, though it's rare that there's no bias at all. Before concluding there's no bias in the pipeline, consider exploring other hypotheses._ +_You might disprove the hypothesis, though it's rare that there's no bias at all._ 2. **Bias was detected - it has been fully mitigated** _It's unlikely, but possible. If this happens, ensure the impact on overall performance and any further bias is thoroughly investigated and clearly explained._ @@ -89,6 +93,6 @@ If you conclude that bias has been detected and it can only be partially mitigat This approach doesn’t aim to make statements about the _impact_ of the detected bias. In other words, detecting bias alone doesn’t allow you to draw conclusions on how the resulting linked data will look. This is because many other unrelated factors in the pipeline will interact with the scenarios and affect whether records are linked or not. -Therefore, the goal here is to better understand bias. This is done by highlighting specific scenarios, examining how the model handles them, and introducing mitigations directly. This approach allows for more transparent statements about bias during the development of a linkage pipeline. +The goal here is to better understand bias. This is done by highlighting specific scenarios, examining how the model handles them, and introducing mitigations directly. This approach allows for more transparent statements about bias during the development of a linkage pipeline. -If you have any ideas or feedback on this process or how it's working in the notebook - **please let us know!** \ No newline at end of file +If you have any ideas or feedback on this process or how it's working in the notebook - **[please let us know!](https://github.com/moj-analytical-services/splink/discussions/new/choose)** \ No newline at end of file From 373f62cc243828e487f99a55112e2694c72d2426 Mon Sep 17 00:00:00 2001 From: Ross Kennedy Date: Mon, 2 Dec 2024 14:35:22 +0000 Subject: [PATCH 09/14] update bias blog and add notebook --- docs/blog/posts/2024-08-15-bias-continued.md | 24 +- docs/demos/examples/duckdb/bias_eval.ipynb | 238 +++------ .../examples/duckdb/model_create_h50k.ipynb | 451 ++++++++++++++++++ docs/demos/examples/duckdb/model_h50k.json | 280 +++++++++++ 4 files changed, 801 insertions(+), 192 deletions(-) create mode 100644 docs/demos/examples/duckdb/model_create_h50k.ipynb create mode 100644 docs/demos/examples/duckdb/model_h50k.json diff --git a/docs/blog/posts/2024-08-15-bias-continued.md b/docs/blog/posts/2024-08-15-bias-continued.md index ee64f322c6..d80c670360 100644 --- a/docs/blog/posts/2024-08-15-bias-continued.md +++ b/docs/blog/posts/2024-08-15-bias-continued.md @@ -8,29 +8,25 @@ categories: # Bias in Data Linking, continued -This Splink Blog is the second installment dedicated to the topic of bias. It wraps up the work completed during the the six-month [Alan Turing Institute](https://www.turing.ac.uk) internship on '_Bias in Data Linking_', summarising the final thoughts. +This blog is the second in our series dedicated to Bias in Data Linking. Here we wrap up work completed during the the six-month [Alan Turing Institute](https://www.turing.ac.uk) internship on '_Bias in Data Linking_', and share some final thoughts. -!!! note - - This blog is intentionally high-level, with specific Splink examples in code provided in the [accompanying notebook](../../demos/examples/duckdb/bias_eval.ipynb). - -## ⏪ Recap +In the [previous blog post](2024-07-11-bias.md) we concluded that assessing bias in a linkage pipeline using real data and clerical labels is possible to some degree, but has major shortcomings. Here we consider a different approach which aims to address some of these issues. -This blog builds upon the [previous post in this series](https://moj-analytical-services.github.io/splink/blog/2024/08/19/bias-in-data-linking.html). +## A Different Approach -In the first few months of the internship, the focus was on exploring potential sources of bias in data linking pipelines. This highlighted the complexity of these biases and the need for a standardised approach to evaluate them. As a result, a goal was set to develop such an approach. +[Previously](2024-07-11-bias.md#-sources-of-bias) we established that bias in a linkage pipeline stems from two main sources: the **data** being linked and the **linkage model**. Considering both of these factors at the same time is complex, making it difficult to reach solid conclusions. Here we will explore bias how bias can have an impact within the **linkage model only**. -To achieve this, a common performance evaluation method using clerical review was examined to see how it could be adapted for bias assessment. However, challenges arose with this method, primarily due to difficulties in using real data to detect bias in the messy linkage output. +As before, we assume there is a hypothesis that we would like to explore. In order to target bias within the linkage model, we construct a synthetic dataset with combinations of records which reflect the hypothesis we wish to test. -## ⏩ Recent developments +This approach can be broken down into 5 key steps: -Building upon these challenges, it was determined that the output of a linkage pipeline (i.e. the linked data) shouldn’t be used to _detect_ bias within a pipeline. Instead, it can be used to understand the _impact_ of any detected bias. +![Image 1](./img/bias_investigation_steps.png) -Detecting bias should be guided by a hypothesis to form a key part of understanding any data linkage pipeline. This blog proposes a high-level approach to bias detection, broken down into 5 key steps: +!!! note -![Image 1](./img/bias_investigation_steps.png) + The following steps are intentionally high-level, for a more hands-on application on fake data with Splink check out the [accompanying example notebook](../../demos/examples/duckdb/bias_eval.ipynb). ## 1. Generate synthetic records @@ -56,7 +52,7 @@ Given the final linkage score relies on the accumulation of these match weights, Now that you have records to compare and a trained model, the next stage is to generate match probabilities. If a threshold for a link has been chosen, this can be used to indicate which records would be considered to be a link in a linkage pipeline. -It’s also useful to examine each comparison to see which features impact the match probability the most. In Splink, you can use a [waterfall chart](https://moj-analytical-services.github.io/splink/charts/waterfall_chart.html) for this. This will help you identify if any weights are too predictive or not predictive enough based on your hypothesis. +It's also useful to examine each comparison to see which features impact the match probability the most. In Splink, you can use a [waterfall chart](https://moj-analytical-services.github.io/splink/charts/waterfall_chart.html) for this. This will help you identify if any weights are too predictive or not predictive enough based on your hypothesis. Some factor weightings might seem off for your hypothesis but be reasonable for the [overall model](#2-train-and-investigate-model). They might not be _wrong_ per se, but if they create issues in specific scenarios, they will **introduce bias into the pipeline**. diff --git a/docs/demos/examples/duckdb/bias_eval.ipynb b/docs/demos/examples/duckdb/bias_eval.ipynb index ebc0b3a981..848540f75a 100644 --- a/docs/demos/examples/duckdb/bias_eval.ipynb +++ b/docs/demos/examples/duckdb/bias_eval.ipynb @@ -4,7 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### **Bias detection**\n", + "!!! note\n", + "\n", + " This notebook is intended for usage alongside the second [Bias in Data Linking blog.](../../../blog/posts/2024-08-15-sd.md)\n", "\n", "This notebook will guide you through a 5-step process for evaluating bias in a data linking pipeline. It offers an isolated approach to **bias detection**, exploring potential mitigation options, before drawing conclusions about the existence of bias in the pipeline.\n", "\n", @@ -21,7 +23,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### **1. Generate synthetic data**\n", + "### **1. Generate synthetic data**\n", "\n", "Since the hypothesis deals with changes in surnames and addresses, the synthetic data should include multiple records for the same person with these variations:\n", "\n", @@ -30,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -54,7 +56,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -114,7 +116,7 @@ "0 ba12 0ay female politician " ] }, - "execution_count": 4, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -125,7 +127,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -241,7 +243,7 @@ "4 ba12 0ay female artist " ] }, - "execution_count": 5, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -265,7 +267,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### **2. Train and investigate model**\n", + "### **2. Train and investigate model**\n", "\n", "Train the model on the real production data (this Splink model is trained in 'model_create_h50k.ipynb' and saved as a json file). \n", "You can refer to the [Splink tutorial](https://moj-analytical-services.github.io/splink/demos/tutorials/00_Tutorial_Introduction.html) if you need any further explanation on how to prepare the data and define the model settings. " @@ -273,9 +275,18 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "downloading: https://raw.githubusercontent.com/moj-analytical-services/splink_datasets/master/data/historical_figures_with_errors_50k.parquet\n", + "\n" + ] + } + ], "source": [ "from splink import DuckDBAPI\n", "from splink import Linker, SettingsCreator\n", @@ -288,7 +299,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -306,7 +317,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -314,28 +325,28 @@ "text/html": [ "\n", "\n", - "
\n", + "
\n", "" ], "text/plain": [ "alt.VConcatChart(...)" ] }, - "execution_count": 8, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -412,14 +423,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### **3. Perform and evaluate linkage**\n", + "### **3. Perform and evaluate linkage**\n", "\n", "In a standard linkage with Splink, all records which meet the criteria of the blocking rules would be compared against each other. However, in bias detection we may not be interested in a lot of the comparisons this process will generate match probabilities for. It can be easier to manually generate the comparisons that are relevant to the hypothesis. " ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -436,7 +447,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -480,7 +491,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -498,151 +509,20 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "metadata": {}, "outputs": [ { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
 match_weightmatch_probabilityunique_id_lunique_id_rfirst_name_lfirst_name_rsurname_lsurname_rdob_ldob_rpostcode_fake_lpostcode_fake_rbirth_place_lbirth_place_roccupation_loccupation_r
035.0457941.00000012sarahsarahbrownbrown1862-07-111862-07-11ba12 0ayba12 0aylondonlondonpoliticianpolitician
121.1302631.00000013sarahsarahbrowndoyle1862-07-111862-07-11ba12 0ayba12 0aylondonlondonpoliticianpolitician
219.4522600.99999914sarahsarahbrownbrown1862-07-111862-07-11ba12 0aytf3 2nglondonlondonpoliticianpolitician
35.5367280.97891315sarahsarahbrowndoyle1862-07-111862-07-11ba12 0aytf3 2nglondonlondonpoliticianpolitician
48.0468010.99623316sarahjanebrownbrown1862-07-111860-01-01ba12 0ayba12 0aylondonlondonpoliticianartist
\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" + "ename": "AttributeError", + "evalue": "'DataFrame' object has no attribute 'map'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn [11], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m columns_of_interest \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmatch_weight\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmatch_probability\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124munique_id_l\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124munique_id_r\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfirst_name_l\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfirst_name_r\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msurname_l\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msurname_r\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdob_l\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdob_r\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpostcode_fake_l\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpostcode_fake_r\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbirth_place_l\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbirth_place_r\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124moccupation_l\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124moccupation_r\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m----> 2\u001b[0m comparisons[columns_of_interest]\u001b[38;5;241m.\u001b[39mmap(highlight_cells, subset\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmatch_probability\u001b[39m\u001b[38;5;124m'\u001b[39m])\n", + "File \u001b[0;32m~/Library/r-miniconda/lib/python3.9/site-packages/pandas/core/generic.py:5902\u001b[0m, in \u001b[0;36mNDFrame.__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 5895\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m 5896\u001b[0m name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_internal_names_set\n\u001b[1;32m 5897\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_metadata\n\u001b[1;32m 5898\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_accessors\n\u001b[1;32m 5899\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_info_axis\u001b[38;5;241m.\u001b[39m_can_hold_identifiers_and_holds_name(name)\n\u001b[1;32m 5900\u001b[0m ):\n\u001b[1;32m 5901\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m[name]\n\u001b[0;32m-> 5902\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mobject\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__getattribute__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'map'" + ] } ], "source": [ @@ -755,11 +635,15 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Taking a closer look at each comparison helps reveal which features have the most impact in terms of the hypothesis. \n", + "Taking a closer look at each comparison helps reveal which features have the most impact in terms of the hypothesis. Some key takeaways are:\n", "\n", - "Within the model, matching on postcode and surname are the strongest indicators of a link. Therefore, when both surname and postcode change, you lose that strong link advantage. So, it's not that a non-match on surname or postcode is overly detrimental to the records being linked, but without the boost from a surname or postcode match, they only meet the low linkage threshold. In contrast, in the sibling scenario, you can see the impact of one or two strong predictors in a model can have. Even with three non-matches (first name, DOB, and occupation), matching on both postcode and surname links the records at a medium threshold. Also, first name matches are less predictive than surname matches - so the sibling scenario has a more predictive name match than changing information. \n", + "- Matching on postcode and surname are the strongest indicators of a link. Therefore, when both surname and postcode change, you lose that strong link advantage. It's not that a non-match on surname or postcode is overly detrimental to the records being linked, but without the boost from a surname or postcode match, they only meet the low linkage threshold. \n", "\n", - "Due to the way individual factors are weighted, the combination of non-matches in this scenario means that people who change both their postcode and surname won't be linked. Since this affects women more than men in our data, it leads to a **gender bias** in the pipeline.\n", + "- In the sibling scenario, you can see the impact of one or two strong predictors in a model can have. Even with three non-matches (first name, DOB, and occupation), matching on both postcode and surname links the records at a medium threshold. \n", + "\n", + "- As first name matches are less predictive than surname matches the sibling scenario has a more predictive name match than the same person changing their information. \n", + "\n", + "- Due to the way individual factors are weighted, the combination of non-matches in this scenario means that people who change both their postcode and surname won't be linked at higher thresholds. Given this affects women more than men in our data, it leads to a **gender bias** in the pipeline.\n", "\n", "---" ] @@ -768,7 +652,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### **4. Identify mitigations**\n", + "### **4. Identify mitigations**\n", "\n", "Now that bias has been detected, it's time to explore potential mitigation strategies:\n", "\n", @@ -792,7 +676,7 @@ "\n", "We'd need to decide that a factor’s predictive power doesn't fit our hypothesis - but also make sure this applies across the whole model. There are lots of options, like making surname or postcode less predictive, or boosting factors like occupation, first name, DOB, or birthplace. We'll focus on one example to demonstrate the necessary considerations. \n", "\n", - "Say we decide to make first name as predictive as surname, and we find that this change won’t impact overall performance. Therefore, we can take this technical solution to the next consideration.\n", + "Say we decide to manually make first name as predictive as surname, and we find that this change won’t impact overall performance. Therefore, we can take this technical solution to the next consideration.\n", "\n", "Could they introduce further bias?\n", "\n", @@ -809,7 +693,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### **5. Make a statement about bias** \n", + "### **5. Make a statement about bias** \n", "\n", "When a bias is detected but not mitigated, it’s crucial to explore it further to gain more insight. This helps refine the understanding of the bias and lays the groundwork for impact assessment.\n", "\n", @@ -977,8 +861,6 @@ "\n", "_Given how the model weighs the different parameters to produce match probabilities, a full change in both surname and postcode will lead to a non-link. As women are more likely than men to make these changes in the input data, this will introduce a gender bias in the pipeline._\n", "\n", - "While some other demographics may be likely to change either names or addresses, a link only fails when both changes occur together. In instances where individuals outside a specific demographic also make both changes, it’s not considered bias — just an impact on overall performance. If this change does affect a specific demographic, it doesn’t negate the bias against women, but adds another layer of bias.\n", - "\n", "Now that this bias has been detected, it's important to note that we can't yet determine its exact impact on the linked data. This is because the final results are influenced by many other factors in the pipeline, which were intentionally excluded from this process to isolate the bias. \n", "\n", "Further investigation is needed to understand impact — such as understanding how many records in the input data undergo these changes, or how many records with a full surname and postcode change end up linked or not linked in the final data. " @@ -987,7 +869,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "base", "language": "python", "name": "python3" }, @@ -1001,7 +883,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.8" + "version": "3.9.12" } }, "nbformat": 4, diff --git a/docs/demos/examples/duckdb/model_create_h50k.ipynb b/docs/demos/examples/duckdb/model_create_h50k.ipynb new file mode 100644 index 0000000000..f1ee5b480c --- /dev/null +++ b/docs/demos/examples/duckdb/model_create_h50k.ipynb @@ -0,0 +1,451 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "#!conda install -c conda-forge splink=4.0 --yes" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from splink import splink_datasets\n", + "\n", + "df = splink_datasets.historical_50k" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from splink import DuckDBAPI\n", + "db_api = DuckDBAPI()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from splink import DuckDBAPI, block_on\n", + "from splink.blocking_analysis import (\n", + " cumulative_comparisons_to_be_scored_from_blocking_rules_chart,\n", + ")\n", + "\n", + "blocking_rules = [\n", + " block_on(\"substr(first_name,1,3)\", \"substr(surname,1,4)\"),\n", + " block_on(\"surname\", \"dob\"),\n", + " block_on(\"first_name\", \"dob\"),\n", + " block_on(\"postcode_fake\", \"first_name\"),\n", + " block_on(\"postcode_fake\", \"surname\"),\n", + " block_on(\"dob\", \"birth_place\"),\n", + " block_on(\"substr(postcode_fake,1,3)\", \"dob\"),\n", + " block_on(\"substr(postcode_fake,1,3)\", \"first_name\"),\n", + " block_on(\"substr(postcode_fake,1,3)\", \"surname\"),\n", + " block_on(\"substr(first_name,1,2)\", \"substr(surname,1,2)\", \"substr(dob,1,4)\"),\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import splink.comparison_library as cl\n", + "\n", + "from splink import Linker, SettingsCreator\n", + "\n", + "settings = SettingsCreator(\n", + " link_type=\"dedupe_only\",\n", + " blocking_rules_to_generate_predictions=blocking_rules,\n", + " comparisons=[\n", + " cl.NameComparison(\"first_name\").configure(term_frequency_adjustments=False),\n", + " cl.NameComparison(\"surname\").configure(term_frequency_adjustments=False),\n", + " cl.DateOfBirthComparison(\"dob\", input_is_string=True),\n", + " cl.PostcodeComparison(\"postcode_fake\"),\n", + " cl.ExactMatch(\"birth_place\").configure(term_frequency_adjustments=False),\n", + " cl.ExactMatch(\"occupation\").configure(term_frequency_adjustments=False),\n", + " ],\n", + " retain_intermediate_calculation_columns=True,\n", + ")\n", + "\n", + "linker = Linker(df, settings, db_api=db_api)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Probability two random records match is estimated to be 0.000136.\n", + "This means that amongst all possible pairwise record comparisons, one in 7,362.31 are expected to match. With 1,279,041,753 total possible comparisons, we expect a total of around 173,728.33 matching pairs\n" + ] + } + ], + "source": [ + "linker.training.estimate_probability_two_random_records_match(\n", + " [\n", + " \"l.first_name = r.first_name and l.surname = r.surname and l.dob = r.dob\",\n", + " \"substr(l.first_name,1,2) = substr(r.first_name,1,2) and l.surname = r.surname and substr(l.postcode_fake,1,2) = substr(r.postcode_fake,1,2)\",\n", + " \"l.dob = r.dob and l.postcode_fake = r.postcode_fake\",\n", + " ],\n", + " recall=0.6,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "----- Estimating u probabilities using random sampling -----\n", + "\n", + "Estimated u probabilities using random sampling\n", + "\n", + "Your model is not yet fully trained. Missing estimates for:\n", + " - first_name (no m values are trained).\n", + " - surname (no m values are trained).\n", + " - dob (no m values are trained).\n", + " - postcode_fake (no m values are trained).\n", + " - birth_place (no m values are trained).\n", + " - occupation (no m values are trained).\n" + ] + } + ], + "source": [ + "linker.training.estimate_u_using_random_sampling(max_pairs=5e6)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "----- Starting EM training session -----\n", + "\n", + "Estimating the m probabilities of the model by blocking on:\n", + "(l.\"first_name\" = r.\"first_name\") AND (l.\"surname\" = r.\"surname\")\n", + "\n", + "Parameter estimates will be made for the following comparison(s):\n", + " - dob\n", + " - postcode_fake\n", + " - birth_place\n", + " - occupation\n", + "\n", + "Parameter estimates cannot be made for the following comparison(s) since they are used in the blocking rules: \n", + " - first_name\n", + " - surname\n", + "\n", + "Iteration 1: Largest change in params was -0.515 in probability_two_random_records_match\n", + "Iteration 2: Largest change in params was -0.0362 in probability_two_random_records_match\n", + "Iteration 3: Largest change in params was 0.0135 in the m_probability of birth_place, level `Exact match on birth_place`\n", + "Iteration 4: Largest change in params was -0.00654 in the m_probability of birth_place, level `All other comparisons`\n", + "Iteration 5: Largest change in params was 0.00378 in the m_probability of birth_place, level `Exact match on birth_place`\n", + "Iteration 6: Largest change in params was -0.00234 in the m_probability of birth_place, level `All other comparisons`\n", + "Iteration 7: Largest change in params was -0.00148 in the m_probability of birth_place, level `All other comparisons`\n", + "Iteration 8: Largest change in params was -0.00095 in the m_probability of dob, level `Abs date difference <= 10 year`\n", + "Iteration 9: Largest change in params was -0.000633 in the m_probability of dob, level `Abs date difference <= 10 year`\n", + "Iteration 10: Largest change in params was -0.000419 in the m_probability of dob, level `Abs date difference <= 10 year`\n", + "Iteration 11: Largest change in params was -0.000277 in the m_probability of dob, level `Abs date difference <= 10 year`\n", + "Iteration 12: Largest change in params was -0.000183 in the m_probability of dob, level `Abs date difference <= 10 year`\n", + "Iteration 13: Largest change in params was -0.00012 in the m_probability of dob, level `Abs date difference <= 10 year`\n", + "Iteration 14: Largest change in params was -7.92e-05 in the m_probability of dob, level `Abs date difference <= 10 year`\n", + "\n", + "EM converged after 14 iterations\n", + "\n", + "Your model is not yet fully trained. Missing estimates for:\n", + " - first_name (no m values are trained).\n", + " - surname (no m values are trained).\n" + ] + } + ], + "source": [ + "training_blocking_rule = block_on(\"first_name\", \"surname\")\n", + "training_session_names = (\n", + " linker.training.estimate_parameters_using_expectation_maximisation(\n", + " training_blocking_rule, estimate_without_term_frequencies=True\n", + " )\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "----- Starting EM training session -----\n", + "\n", + "Estimating the m probabilities of the model by blocking on:\n", + "l.\"dob\" = r.\"dob\"\n", + "\n", + "Parameter estimates will be made for the following comparison(s):\n", + " - first_name\n", + " - surname\n", + " - postcode_fake\n", + " - birth_place\n", + " - occupation\n", + "\n", + "Parameter estimates cannot be made for the following comparison(s) since they are used in the blocking rules: \n", + " - dob\n", + "\n", + "Iteration 1: Largest change in params was -0.36 in the m_probability of first_name, level `Exact match on first_name`\n", + "Iteration 2: Largest change in params was 0.0382 in the m_probability of first_name, level `All other comparisons`\n", + "Iteration 3: Largest change in params was 0.00824 in the m_probability of surname, level `All other comparisons`\n", + "Iteration 4: Largest change in params was 0.00266 in the m_probability of surname, level `All other comparisons`\n", + "Iteration 5: Largest change in params was 0.000806 in the m_probability of surname, level `All other comparisons`\n", + "Iteration 6: Largest change in params was 0.00024 in the m_probability of surname, level `All other comparisons`\n", + "Iteration 7: Largest change in params was 7.1e-05 in the m_probability of surname, level `All other comparisons`\n", + "\n", + "EM converged after 7 iterations\n", + "\n", + "Your model is fully trained. All comparisons have at least one estimate for their m and u values\n" + ] + } + ], + "source": [ + "training_blocking_rule = block_on(\"dob\")\n", + "training_session_dob = (\n", + " linker.training.estimate_parameters_using_expectation_maximisation(\n", + " training_blocking_rule, estimate_without_term_frequencies=True\n", + " )\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'link_type': 'dedupe_only',\n", + " 'probability_two_random_records_match': 0.00013582694460587586,\n", + " 'retain_matching_columns': True,\n", + " 'retain_intermediate_calculation_columns': True,\n", + " 'additional_columns_to_retain': [],\n", + " 'sql_dialect': 'duckdb',\n", + " 'linker_uid': '66au8ius',\n", + " 'em_convergence': 0.0001,\n", + " 'max_iterations': 25,\n", + " 'bayes_factor_column_prefix': 'bf_',\n", + " 'term_frequency_adjustment_column_prefix': 'tf_',\n", + " 'comparison_vector_value_column_prefix': 'gamma_',\n", + " 'unique_id_column_name': 'unique_id',\n", + " 'source_dataset_column_name': 'source_dataset',\n", + " 'blocking_rules_to_generate_predictions': [{'blocking_rule': '(SUBSTR(l.first_name, 1, 3) = SUBSTR(r.first_name, 1, 3)) AND (SUBSTR(l.surname, 1, 4) = SUBSTR(r.surname, 1, 4))',\n", + " 'sql_dialect': 'duckdb'},\n", + " {'blocking_rule': '(l.\"surname\" = r.\"surname\") AND (l.\"dob\" = r.\"dob\")',\n", + " 'sql_dialect': 'duckdb'},\n", + " {'blocking_rule': '(l.\"first_name\" = r.\"first_name\") AND (l.\"dob\" = r.\"dob\")',\n", + " 'sql_dialect': 'duckdb'},\n", + " {'blocking_rule': '(l.\"postcode_fake\" = r.\"postcode_fake\") AND (l.\"first_name\" = r.\"first_name\")',\n", + " 'sql_dialect': 'duckdb'},\n", + " {'blocking_rule': '(l.\"postcode_fake\" = r.\"postcode_fake\") AND (l.\"surname\" = r.\"surname\")',\n", + " 'sql_dialect': 'duckdb'},\n", + " {'blocking_rule': '(l.\"dob\" = r.\"dob\") AND (l.\"birth_place\" = r.\"birth_place\")',\n", + " 'sql_dialect': 'duckdb'},\n", + " {'blocking_rule': '(SUBSTR(l.postcode_fake, 1, 3) = SUBSTR(r.postcode_fake, 1, 3)) AND (l.\"dob\" = r.\"dob\")',\n", + " 'sql_dialect': 'duckdb'},\n", + " {'blocking_rule': '(SUBSTR(l.postcode_fake, 1, 3) = SUBSTR(r.postcode_fake, 1, 3)) AND (l.\"first_name\" = r.\"first_name\")',\n", + " 'sql_dialect': 'duckdb'},\n", + " {'blocking_rule': '(SUBSTR(l.postcode_fake, 1, 3) = SUBSTR(r.postcode_fake, 1, 3)) AND (l.\"surname\" = r.\"surname\")',\n", + " 'sql_dialect': 'duckdb'},\n", + " {'blocking_rule': '(SUBSTR(l.first_name, 1, 2) = SUBSTR(r.first_name, 1, 2)) AND (SUBSTR(l.surname, 1, 2) = SUBSTR(r.surname, 1, 2)) AND (SUBSTR(l.dob, 1, 4) = SUBSTR(r.dob, 1, 4))',\n", + " 'sql_dialect': 'duckdb'}],\n", + " 'comparisons': [{'output_column_name': 'first_name',\n", + " 'comparison_levels': [{'sql_condition': '\"first_name_l\" IS NULL OR \"first_name_r\" IS NULL',\n", + " 'label_for_charts': 'first_name is NULL',\n", + " 'is_null_level': True},\n", + " {'sql_condition': '\"first_name_l\" = \"first_name_r\"',\n", + " 'label_for_charts': 'Exact match on first_name',\n", + " 'm_probability': 0.5457057743059716,\n", + " 'u_probability': 0.011951030823329812,\n", + " 'tf_adjustment_column': 'first_name',\n", + " 'tf_adjustment_weight': 1.0},\n", + " {'sql_condition': 'jaro_winkler_similarity(\"first_name_l\", \"first_name_r\") >= 0.92',\n", + " 'label_for_charts': 'Jaro-Winkler distance of first_name >= 0.92',\n", + " 'm_probability': 0.06098051520443211,\n", + " 'u_probability': 0.0018262673367931644},\n", + " {'sql_condition': 'jaro_winkler_similarity(\"first_name_l\", \"first_name_r\") >= 0.88',\n", + " 'label_for_charts': 'Jaro-Winkler distance of first_name >= 0.88',\n", + " 'm_probability': 0.07502792175772721,\n", + " 'u_probability': 0.003353844214458144},\n", + " {'sql_condition': 'jaro_winkler_similarity(\"first_name_l\", \"first_name_r\") >= 0.7',\n", + " 'label_for_charts': 'Jaro-Winkler distance of first_name >= 0.7',\n", + " 'm_probability': 0.120309986619356,\n", + " 'u_probability': 0.020885448595733304},\n", + " {'sql_condition': 'ELSE',\n", + " 'label_for_charts': 'All other comparisons',\n", + " 'm_probability': 0.19797580211251312,\n", + " 'u_probability': 0.9619834090296856}],\n", + " 'comparison_description': 'NameComparison'},\n", + " {'output_column_name': 'surname',\n", + " 'comparison_levels': [{'sql_condition': '\"surname_l\" IS NULL OR \"surname_r\" IS NULL',\n", + " 'label_for_charts': 'surname is NULL',\n", + " 'is_null_level': True},\n", + " {'sql_condition': '\"surname_l\" = \"surname_r\"',\n", + " 'label_for_charts': 'Exact match on surname',\n", + " 'm_probability': 0.7714400787402513,\n", + " 'u_probability': 0.0006788109515158729,\n", + " 'tf_adjustment_column': 'surname',\n", + " 'tf_adjustment_weight': 1.0},\n", + " {'sql_condition': 'jaro_winkler_similarity(\"surname_l\", \"surname_r\") >= 0.92',\n", + " 'label_for_charts': 'Jaro-Winkler distance of surname >= 0.92',\n", + " 'm_probability': 0.09368190481882498,\n", + " 'u_probability': 0.0002900130152182439},\n", + " {'sql_condition': 'jaro_winkler_similarity(\"surname_l\", \"surname_r\") >= 0.88',\n", + " 'label_for_charts': 'Jaro-Winkler distance of surname >= 0.88',\n", + " 'm_probability': 0.039136395099805245,\n", + " 'u_probability': 0.00048319241644015245},\n", + " {'sql_condition': 'jaro_winkler_similarity(\"surname_l\", \"surname_r\") >= 0.7',\n", + " 'label_for_charts': 'Jaro-Winkler distance of surname >= 0.7',\n", + " 'm_probability': 0.023569512976655334,\n", + " 'u_probability': 0.017232968503952374},\n", + " {'sql_condition': 'ELSE',\n", + " 'label_for_charts': 'All other comparisons',\n", + " 'm_probability': 0.07217210836446314,\n", + " 'u_probability': 0.9813150151128733}],\n", + " 'comparison_description': 'NameComparison'},\n", + " {'output_column_name': 'dob',\n", + " 'comparison_levels': [{'sql_condition': 'try_strptime(\"dob_l\", \\'%Y-%m-%d\\') IS NULL OR try_strptime(\"dob_r\", \\'%Y-%m-%d\\') IS NULL',\n", + " 'label_for_charts': 'transformed dob is NULL',\n", + " 'is_null_level': True},\n", + " {'sql_condition': '\"dob_l\" = \"dob_r\"',\n", + " 'label_for_charts': 'Exact match on date of birth',\n", + " 'm_probability': 0.6796640528638345,\n", + " 'u_probability': 0.0023542362733926883},\n", + " {'sql_condition': 'damerau_levenshtein(\"dob_l\", \"dob_r\") <= 1',\n", + " 'label_for_charts': 'DamerauLevenshtein distance <= 1',\n", + " 'm_probability': 0.2736730864202431,\n", + " 'u_probability': 0.02425500685606134},\n", + " {'sql_condition': 'ABS(EPOCH(try_strptime(\"dob_l\", \\'%Y-%m-%d\\')) - EPOCH(try_strptime(\"dob_r\", \\'%Y-%m-%d\\'))) <= 2629800.0',\n", + " 'label_for_charts': 'Abs date difference <= 1 month',\n", + " 'm_probability': 0.002677966995147039,\n", + " 'u_probability': 0.0023411035361826224},\n", + " {'sql_condition': 'ABS(EPOCH(try_strptime(\"dob_l\", \\'%Y-%m-%d\\')) - EPOCH(try_strptime(\"dob_r\", \\'%Y-%m-%d\\'))) <= 31557600.0',\n", + " 'label_for_charts': 'Abs date difference <= 1 year',\n", + " 'm_probability': 0.006199700352635373,\n", + " 'u_probability': 0.03354178334846173},\n", + " {'sql_condition': 'ABS(EPOCH(try_strptime(\"dob_l\", \\'%Y-%m-%d\\')) - EPOCH(try_strptime(\"dob_r\", \\'%Y-%m-%d\\'))) <= 315576000.0',\n", + " 'label_for_charts': 'Abs date difference <= 10 year',\n", + " 'm_probability': 0.02726057331431288,\n", + " 'u_probability': 0.25042855211572257},\n", + " {'sql_condition': 'ELSE',\n", + " 'label_for_charts': 'All other comparisons',\n", + " 'm_probability': 0.010524620053827111,\n", + " 'u_probability': 0.6870793178701791}],\n", + " 'comparison_description': 'DateOfBirthComparison'},\n", + " {'output_column_name': 'postcode_fake',\n", + " 'comparison_levels': [{'sql_condition': '\"postcode_fake_l\" IS NULL OR \"postcode_fake_r\" IS NULL',\n", + " 'label_for_charts': 'postcode_fake is NULL',\n", + " 'is_null_level': True},\n", + " {'sql_condition': '\"postcode_fake_l\" = \"postcode_fake_r\"',\n", + " 'label_for_charts': 'Exact match on full postcode',\n", + " 'm_probability': 0.6753757141159644,\n", + " 'u_probability': 0.00014903079407788038},\n", + " {'sql_condition': 'NULLIF(regexp_extract(\"postcode_fake_l\", \\'^[A-Za-z]{1,2}[0-9][A-Za-z0-9]? [0-9]\\', 0), \\'\\') = NULLIF(regexp_extract(\"postcode_fake_r\", \\'^[A-Za-z]{1,2}[0-9][A-Za-z0-9]? [0-9]\\', 0), \\'\\')',\n", + " 'label_for_charts': 'Exact match on sector',\n", + " 'm_probability': 0.09395110908506221,\n", + " 'u_probability': 0.00028996941834157726},\n", + " {'sql_condition': 'NULLIF(regexp_extract(\"postcode_fake_l\", \\'^[A-Za-z]{1,2}[0-9][A-Za-z0-9]?\\', 0), \\'\\') = NULLIF(regexp_extract(\"postcode_fake_r\", \\'^[A-Za-z]{1,2}[0-9][A-Za-z0-9]?\\', 0), \\'\\')',\n", + " 'label_for_charts': 'Exact match on district',\n", + " 'm_probability': 0.04225883501370074,\n", + " 'u_probability': 0.00048215845142843657},\n", + " {'sql_condition': 'NULLIF(regexp_extract(\"postcode_fake_l\", \\'^[A-Za-z]{1,2}\\', 0), \\'\\') = NULLIF(regexp_extract(\"postcode_fake_r\", \\'^[A-Za-z]{1,2}\\', 0), \\'\\')',\n", + " 'label_for_charts': 'Exact match on area',\n", + " 'm_probability': 0.09785854822786318,\n", + " 'u_probability': 0.011052555271205699},\n", + " {'sql_condition': 'ELSE',\n", + " 'label_for_charts': 'All other comparisons',\n", + " 'm_probability': 0.09055579355740942,\n", + " 'u_probability': 0.9880262860649464}],\n", + " 'comparison_description': 'PostcodeComparison'},\n", + " {'output_column_name': 'birth_place',\n", + " 'comparison_levels': [{'sql_condition': '\"birth_place_l\" IS NULL OR \"birth_place_r\" IS NULL',\n", + " 'label_for_charts': 'birth_place is NULL',\n", + " 'is_null_level': True},\n", + " {'sql_condition': '\"birth_place_l\" = \"birth_place_r\"',\n", + " 'label_for_charts': 'Exact match on birth_place',\n", + " 'm_probability': 0.8345399212327826,\n", + " 'u_probability': 0.005228068621252938},\n", + " {'sql_condition': 'ELSE',\n", + " 'label_for_charts': 'All other comparisons',\n", + " 'm_probability': 0.1654600787672175,\n", + " 'u_probability': 0.9947719313787471}],\n", + " 'comparison_description': 'ExactMatch'},\n", + " {'output_column_name': 'occupation',\n", + " 'comparison_levels': [{'sql_condition': '\"occupation_l\" IS NULL OR \"occupation_r\" IS NULL',\n", + " 'label_for_charts': 'occupation is NULL',\n", + " 'is_null_level': True},\n", + " {'sql_condition': '\"occupation_l\" = \"occupation_r\"',\n", + " 'label_for_charts': 'Exact match on occupation',\n", + " 'm_probability': 0.8982446653774181,\n", + " 'u_probability': 0.037284666827814034},\n", + " {'sql_condition': 'ELSE',\n", + " 'label_for_charts': 'All other comparisons',\n", + " 'm_probability': 0.10175533462258204,\n", + " 'u_probability': 0.9627153331721859}],\n", + " 'comparison_description': 'ExactMatch'}]}" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "linker.misc.save_model_to_json(\"model_h50k.json\", overwrite=True)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/demos/examples/duckdb/model_h50k.json b/docs/demos/examples/duckdb/model_h50k.json new file mode 100644 index 0000000000..a1e438445d --- /dev/null +++ b/docs/demos/examples/duckdb/model_h50k.json @@ -0,0 +1,280 @@ +{ + "link_type": "dedupe_only", + "probability_two_random_records_match": 0.00013582694460587586, + "retain_matching_columns": true, + "retain_intermediate_calculation_columns": true, + "additional_columns_to_retain": [], + "sql_dialect": "duckdb", + "linker_uid": "66au8ius", + "em_convergence": 0.0001, + "max_iterations": 25, + "bayes_factor_column_prefix": "bf_", + "term_frequency_adjustment_column_prefix": "tf_", + "comparison_vector_value_column_prefix": "gamma_", + "unique_id_column_name": "unique_id", + "source_dataset_column_name": "source_dataset", + "blocking_rules_to_generate_predictions": [ + { + "blocking_rule": "(SUBSTR(l.first_name, 1, 3) = SUBSTR(r.first_name, 1, 3)) AND (SUBSTR(l.surname, 1, 4) = SUBSTR(r.surname, 1, 4))", + "sql_dialect": "duckdb" + }, + { + "blocking_rule": "(l.\"surname\" = r.\"surname\") AND (l.\"dob\" = r.\"dob\")", + "sql_dialect": "duckdb" + }, + { + "blocking_rule": "(l.\"first_name\" = r.\"first_name\") AND (l.\"dob\" = r.\"dob\")", + "sql_dialect": "duckdb" + }, + { + "blocking_rule": "(l.\"postcode_fake\" = r.\"postcode_fake\") AND (l.\"first_name\" = r.\"first_name\")", + "sql_dialect": "duckdb" + }, + { + "blocking_rule": "(l.\"postcode_fake\" = r.\"postcode_fake\") AND (l.\"surname\" = r.\"surname\")", + "sql_dialect": "duckdb" + }, + { + "blocking_rule": "(l.\"dob\" = r.\"dob\") AND (l.\"birth_place\" = r.\"birth_place\")", + "sql_dialect": "duckdb" + }, + { + "blocking_rule": "(SUBSTR(l.postcode_fake, 1, 3) = SUBSTR(r.postcode_fake, 1, 3)) AND (l.\"dob\" = r.\"dob\")", + "sql_dialect": "duckdb" + }, + { + "blocking_rule": "(SUBSTR(l.postcode_fake, 1, 3) = SUBSTR(r.postcode_fake, 1, 3)) AND (l.\"first_name\" = r.\"first_name\")", + "sql_dialect": "duckdb" + }, + { + "blocking_rule": "(SUBSTR(l.postcode_fake, 1, 3) = SUBSTR(r.postcode_fake, 1, 3)) AND (l.\"surname\" = r.\"surname\")", + "sql_dialect": "duckdb" + }, + { + "blocking_rule": "(SUBSTR(l.first_name, 1, 2) = SUBSTR(r.first_name, 1, 2)) AND (SUBSTR(l.surname, 1, 2) = SUBSTR(r.surname, 1, 2)) AND (SUBSTR(l.dob, 1, 4) = SUBSTR(r.dob, 1, 4))", + "sql_dialect": "duckdb" + } + ], + "comparisons": [ + { + "output_column_name": "first_name", + "comparison_levels": [ + { + "sql_condition": "\"first_name_l\" IS NULL OR \"first_name_r\" IS NULL", + "label_for_charts": "first_name is NULL", + "is_null_level": true + }, + { + "sql_condition": "\"first_name_l\" = \"first_name_r\"", + "label_for_charts": "Exact match on first_name", + "m_probability": 0.5457057743059716, + "u_probability": 0.011951030823329812, + "tf_adjustment_column": "first_name", + "tf_adjustment_weight": 1.0 + }, + { + "sql_condition": "jaro_winkler_similarity(\"first_name_l\", \"first_name_r\") >= 0.92", + "label_for_charts": "Jaro-Winkler distance of first_name >= 0.92", + "m_probability": 0.06098051520443211, + "u_probability": 0.0018262673367931644 + }, + { + "sql_condition": "jaro_winkler_similarity(\"first_name_l\", \"first_name_r\") >= 0.88", + "label_for_charts": "Jaro-Winkler distance of first_name >= 0.88", + "m_probability": 0.07502792175772721, + "u_probability": 0.003353844214458144 + }, + { + "sql_condition": "jaro_winkler_similarity(\"first_name_l\", \"first_name_r\") >= 0.7", + "label_for_charts": "Jaro-Winkler distance of first_name >= 0.7", + "m_probability": 0.120309986619356, + "u_probability": 0.020885448595733304 + }, + { + "sql_condition": "ELSE", + "label_for_charts": "All other comparisons", + "m_probability": 0.19797580211251312, + "u_probability": 0.9619834090296856 + } + ], + "comparison_description": "NameComparison" + }, + { + "output_column_name": "surname", + "comparison_levels": [ + { + "sql_condition": "\"surname_l\" IS NULL OR \"surname_r\" IS NULL", + "label_for_charts": "surname is NULL", + "is_null_level": true + }, + { + "sql_condition": "\"surname_l\" = \"surname_r\"", + "label_for_charts": "Exact match on surname", + "m_probability": 0.7714400787402513, + "u_probability": 0.0006788109515158729, + "tf_adjustment_column": "surname", + "tf_adjustment_weight": 1.0 + }, + { + "sql_condition": "jaro_winkler_similarity(\"surname_l\", \"surname_r\") >= 0.92", + "label_for_charts": "Jaro-Winkler distance of surname >= 0.92", + "m_probability": 0.09368190481882498, + "u_probability": 0.0002900130152182439 + }, + { + "sql_condition": "jaro_winkler_similarity(\"surname_l\", \"surname_r\") >= 0.88", + "label_for_charts": "Jaro-Winkler distance of surname >= 0.88", + "m_probability": 0.039136395099805245, + "u_probability": 0.00048319241644015245 + }, + { + "sql_condition": "jaro_winkler_similarity(\"surname_l\", \"surname_r\") >= 0.7", + "label_for_charts": "Jaro-Winkler distance of surname >= 0.7", + "m_probability": 0.023569512976655334, + "u_probability": 0.017232968503952374 + }, + { + "sql_condition": "ELSE", + "label_for_charts": "All other comparisons", + "m_probability": 0.07217210836446314, + "u_probability": 0.9813150151128733 + } + ], + "comparison_description": "NameComparison" + }, + { + "output_column_name": "dob", + "comparison_levels": [ + { + "sql_condition": "try_strptime(\"dob_l\", '%Y-%m-%d') IS NULL OR try_strptime(\"dob_r\", '%Y-%m-%d') IS NULL", + "label_for_charts": "transformed dob is NULL", + "is_null_level": true + }, + { + "sql_condition": "\"dob_l\" = \"dob_r\"", + "label_for_charts": "Exact match on date of birth", + "m_probability": 0.6796640528638345, + "u_probability": 0.0023542362733926883 + }, + { + "sql_condition": "damerau_levenshtein(\"dob_l\", \"dob_r\") <= 1", + "label_for_charts": "DamerauLevenshtein distance <= 1", + "m_probability": 0.2736730864202431, + "u_probability": 0.02425500685606134 + }, + { + "sql_condition": "ABS(EPOCH(try_strptime(\"dob_l\", '%Y-%m-%d')) - EPOCH(try_strptime(\"dob_r\", '%Y-%m-%d'))) <= 2629800.0", + "label_for_charts": "Abs date difference <= 1 month", + "m_probability": 0.002677966995147039, + "u_probability": 0.0023411035361826224 + }, + { + "sql_condition": "ABS(EPOCH(try_strptime(\"dob_l\", '%Y-%m-%d')) - EPOCH(try_strptime(\"dob_r\", '%Y-%m-%d'))) <= 31557600.0", + "label_for_charts": "Abs date difference <= 1 year", + "m_probability": 0.006199700352635373, + "u_probability": 0.03354178334846173 + }, + { + "sql_condition": "ABS(EPOCH(try_strptime(\"dob_l\", '%Y-%m-%d')) - EPOCH(try_strptime(\"dob_r\", '%Y-%m-%d'))) <= 315576000.0", + "label_for_charts": "Abs date difference <= 10 year", + "m_probability": 0.02726057331431288, + "u_probability": 0.25042855211572257 + }, + { + "sql_condition": "ELSE", + "label_for_charts": "All other comparisons", + "m_probability": 0.010524620053827111, + "u_probability": 0.6870793178701791 + } + ], + "comparison_description": "DateOfBirthComparison" + }, + { + "output_column_name": "postcode_fake", + "comparison_levels": [ + { + "sql_condition": "\"postcode_fake_l\" IS NULL OR \"postcode_fake_r\" IS NULL", + "label_for_charts": "postcode_fake is NULL", + "is_null_level": true + }, + { + "sql_condition": "\"postcode_fake_l\" = \"postcode_fake_r\"", + "label_for_charts": "Exact match on full postcode", + "m_probability": 0.6753757141159644, + "u_probability": 0.00014903079407788038 + }, + { + "sql_condition": "NULLIF(regexp_extract(\"postcode_fake_l\", '^[A-Za-z]{1,2}[0-9][A-Za-z0-9]? [0-9]', 0), '') = NULLIF(regexp_extract(\"postcode_fake_r\", '^[A-Za-z]{1,2}[0-9][A-Za-z0-9]? [0-9]', 0), '')", + "label_for_charts": "Exact match on sector", + "m_probability": 0.09395110908506221, + "u_probability": 0.00028996941834157726 + }, + { + "sql_condition": "NULLIF(regexp_extract(\"postcode_fake_l\", '^[A-Za-z]{1,2}[0-9][A-Za-z0-9]?', 0), '') = NULLIF(regexp_extract(\"postcode_fake_r\", '^[A-Za-z]{1,2}[0-9][A-Za-z0-9]?', 0), '')", + "label_for_charts": "Exact match on district", + "m_probability": 0.04225883501370074, + "u_probability": 0.00048215845142843657 + }, + { + "sql_condition": "NULLIF(regexp_extract(\"postcode_fake_l\", '^[A-Za-z]{1,2}', 0), '') = NULLIF(regexp_extract(\"postcode_fake_r\", '^[A-Za-z]{1,2}', 0), '')", + "label_for_charts": "Exact match on area", + "m_probability": 0.09785854822786318, + "u_probability": 0.011052555271205699 + }, + { + "sql_condition": "ELSE", + "label_for_charts": "All other comparisons", + "m_probability": 0.09055579355740942, + "u_probability": 0.9880262860649464 + } + ], + "comparison_description": "PostcodeComparison" + }, + { + "output_column_name": "birth_place", + "comparison_levels": [ + { + "sql_condition": "\"birth_place_l\" IS NULL OR \"birth_place_r\" IS NULL", + "label_for_charts": "birth_place is NULL", + "is_null_level": true + }, + { + "sql_condition": "\"birth_place_l\" = \"birth_place_r\"", + "label_for_charts": "Exact match on birth_place", + "m_probability": 0.8345399212327826, + "u_probability": 0.005228068621252938 + }, + { + "sql_condition": "ELSE", + "label_for_charts": "All other comparisons", + "m_probability": 0.1654600787672175, + "u_probability": 0.9947719313787471 + } + ], + "comparison_description": "ExactMatch" + }, + { + "output_column_name": "occupation", + "comparison_levels": [ + { + "sql_condition": "\"occupation_l\" IS NULL OR \"occupation_r\" IS NULL", + "label_for_charts": "occupation is NULL", + "is_null_level": true + }, + { + "sql_condition": "\"occupation_l\" = \"occupation_r\"", + "label_for_charts": "Exact match on occupation", + "m_probability": 0.8982446653774181, + "u_probability": 0.037284666827814034 + }, + { + "sql_condition": "ELSE", + "label_for_charts": "All other comparisons", + "m_probability": 0.10175533462258204, + "u_probability": 0.9627153331721859 + } + ], + "comparison_description": "ExactMatch" + } + ] +} \ No newline at end of file From c7f6ff3546a27c8d90fe999574bde7df98f71bfa Mon Sep 17 00:00:00 2001 From: Ross Kennedy Date: Tue, 3 Dec 2024 17:07:51 +0000 Subject: [PATCH 10/14] Move data prep files and fix notebook run --- docs/blog/posts/2024-08-15-bias-continued.md | 3 +- .../model_create_h50k.ipynb | 0 .../duckdb => demo_settings}/model_h50k.json | 0 docs/demos/examples/duckdb/bias_eval.ipynb | 374 ++++++++++++------ 4 files changed, 250 insertions(+), 127 deletions(-) rename docs/demos/{examples/duckdb => demo_settings}/model_create_h50k.ipynb (100%) rename docs/demos/{examples/duckdb => demo_settings}/model_h50k.json (100%) diff --git a/docs/blog/posts/2024-08-15-bias-continued.md b/docs/blog/posts/2024-08-15-bias-continued.md index d80c670360..61e1796593 100644 --- a/docs/blog/posts/2024-08-15-bias-continued.md +++ b/docs/blog/posts/2024-08-15-bias-continued.md @@ -1,7 +1,8 @@ --- -date: 2024-09-19 +date: 2024-12-02 authors: - erica-k + - ross-k categories: - Bias --- diff --git a/docs/demos/examples/duckdb/model_create_h50k.ipynb b/docs/demos/demo_settings/model_create_h50k.ipynb similarity index 100% rename from docs/demos/examples/duckdb/model_create_h50k.ipynb rename to docs/demos/demo_settings/model_create_h50k.ipynb diff --git a/docs/demos/examples/duckdb/model_h50k.json b/docs/demos/demo_settings/model_h50k.json similarity index 100% rename from docs/demos/examples/duckdb/model_h50k.json rename to docs/demos/demo_settings/model_h50k.json diff --git a/docs/demos/examples/duckdb/bias_eval.ipynb b/docs/demos/examples/duckdb/bias_eval.ipynb index 848540f75a..17c23659f6 100644 --- a/docs/demos/examples/duckdb/bias_eval.ipynb +++ b/docs/demos/examples/duckdb/bias_eval.ipynb @@ -32,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -56,7 +56,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -116,7 +116,7 @@ "0 ba12 0ay female politician " ] }, - "execution_count": 2, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -127,7 +127,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -243,7 +243,7 @@ "4 ba12 0ay female artist " ] }, - "execution_count": 3, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -275,18 +275,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 9, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "downloading: https://raw.githubusercontent.com/moj-analytical-services/splink_datasets/master/data/historical_figures_with_errors_50k.parquet\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "from splink import DuckDBAPI\n", "from splink import Linker, SettingsCreator\n", @@ -299,11 +290,11 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ - "linker = Linker(production_df, settings='model_h50k.json', db_api=db_api)" + "linker = Linker(production_df, settings='../../demo_settings/model_h50k.json', db_api=db_api)" ] }, { @@ -317,7 +308,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -325,23 +316,23 @@ "text/html": [ "\n", "\n", - "
\n", + "
\n", "" ], "text/plain": [ "alt.LayerChart(...)" ] }, - "execution_count": 12, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -702,7 +824,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -733,8 +855,6 @@ "name": "stderr", "output_type": "stream", "text": [ - "No term frequencies found for column \"surname\".\n", - "To apply term frequency adjustments, you need to register a lookup using `linker.table_management.register_term_frequency_lookup`.\n", "No term frequencies found for column \"first_name\".\n", "To apply term frequency adjustments, you need to register a lookup using `linker.table_management.register_term_frequency_lookup`.\n", "No term frequencies found for column \"surname\".\n", @@ -744,6 +864,8 @@ "No term frequencies found for column \"surname\".\n", "To apply term frequency adjustments, you need to register a lookup using `linker.table_management.register_term_frequency_lookup`.\n", "No term frequencies found for column \"first_name\".\n", + "To apply term frequency adjustments, you need to register a lookup using `linker.table_management.register_term_frequency_lookup`.\n", + "No term frequencies found for column \"surname\".\n", "To apply term frequency adjustments, you need to register a lookup using `linker.table_management.register_term_frequency_lookup`.\n" ] }, @@ -751,95 +873,95 @@ "data": { "text/html": [ "\n", - "\n", + "
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015.6417000.99998012sarahsarahbrownbrown-doyle1862-07-111862-07-11ba12 0aytf3 2nglondonlondonpoliticianpolitician015.6417000.99998012sarahsarahbrownbrown-doyle1862-07-111862-07-11ba12 0aytf3 2nglondonlondonpoliticianpolitician
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\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 18, From 33fafa9489824a175183e8205203f1c74bef1269 Mon Sep 17 00:00:00 2001 From: Ross Kennedy Date: Wed, 4 Dec 2024 10:42:54 +0100 Subject: [PATCH 11/14] Update docs/blog/posts/2024-08-15-bias-continued.md Co-authored-by: Sam Lindsay --- docs/blog/posts/2024-08-15-bias-continued.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/blog/posts/2024-08-15-bias-continued.md b/docs/blog/posts/2024-08-15-bias-continued.md index 61e1796593..f3d1c50ee8 100644 --- a/docs/blog/posts/2024-08-15-bias-continued.md +++ b/docs/blog/posts/2024-08-15-bias-continued.md @@ -17,7 +17,7 @@ In the [previous blog post](2024-07-11-bias.md) we concluded that assessing bias ## A Different Approach -[Previously](2024-07-11-bias.md#-sources-of-bias) we established that bias in a linkage pipeline stems from two main sources: the **data** being linked and the **linkage model**. Considering both of these factors at the same time is complex, making it difficult to reach solid conclusions. Here we will explore bias how bias can have an impact within the **linkage model only**. +[Previously](2024-07-11-bias.md#-sources-of-bias) we established that bias in a linkage pipeline stems from two main sources: the **data** being linked and the **linkage model**. Considering both of these factors at the same time is complex, making it difficult to reach solid conclusions. Here we will explore how bias can have an impact within the **linkage model only**. As before, we assume there is a hypothesis that we would like to explore. In order to target bias within the linkage model, we construct a synthetic dataset with combinations of records which reflect the hypothesis we wish to test. From 58d202c2dd55974dbe4481f1ef987b888b10934d Mon Sep 17 00:00:00 2001 From: Ross Kennedy Date: Wed, 4 Dec 2024 10:43:09 +0100 Subject: [PATCH 12/14] Update docs/blog/posts/2024-08-15-bias-continued.md Co-authored-by: Sam Lindsay --- docs/blog/posts/2024-08-15-bias-continued.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/blog/posts/2024-08-15-bias-continued.md b/docs/blog/posts/2024-08-15-bias-continued.md index f3d1c50ee8..0930cc6bf1 100644 --- a/docs/blog/posts/2024-08-15-bias-continued.md +++ b/docs/blog/posts/2024-08-15-bias-continued.md @@ -37,7 +37,7 @@ Instead of focussing on the number of synthetic records, the key is ensuring eac ![Image 2](./img/sp_mistake_data.png) -This is a simplification, and a real generation would likely include more rows with various spelling error iterations. The final two record also consider the case where similarly spelt names that relate to different individuals. +This is a simplification, and a real generation would likely include more rows with various spelling error iterations. The final two records also consider the case of similarly spelled names relating to different individuals. ## 2. Investigate model parameters From 9a6e3ac58496a7cb87ae39494194b3a028c716e1 Mon Sep 17 00:00:00 2001 From: Ross Kennedy Date: Wed, 4 Dec 2024 10:43:21 +0100 Subject: [PATCH 13/14] Update docs/blog/posts/2024-08-15-bias-continued.md Co-authored-by: Sam Lindsay --- docs/blog/posts/2024-08-15-bias-continued.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/blog/posts/2024-08-15-bias-continued.md b/docs/blog/posts/2024-08-15-bias-continued.md index 0930cc6bf1..2ae97dc39c 100644 --- a/docs/blog/posts/2024-08-15-bias-continued.md +++ b/docs/blog/posts/2024-08-15-bias-continued.md @@ -45,7 +45,7 @@ The pre-trained model can be analysed specifically in relation to the hypothesis ![Image 3](./img/match_weights_chart.png) -Each of the bars represent the amount of evidence (match weights) that two records to refer to the same individual depending on how similar each of the feature are. It's useful to start thinking about how this chart relates to the scenario represented by the synthetic records. For example, for the above model there is only a significant drop in match weight for first_name between the third and fourth comparison level. +Each of the bars represent the amount of evidence (match weights) that two records refer to the same individual, depending on how similar the features are. It's useful to start thinking about how this chart relates to the scenario represented by the synthetic records. For example, for the above model there is only a significant drop in match weight for first_name between the third and fourth comparison level. Given the final linkage score relies on the accumulation of these match weights, viewing the model parameters in isolation like this does not give the full picture - specific examples are required. From 8d39344cd0d940a206f158b3bd7a4ae60388e7ac Mon Sep 17 00:00:00 2001 From: Ross Kennedy Date: Wed, 4 Dec 2024 10:43:31 +0100 Subject: [PATCH 14/14] Update docs/blog/posts/2024-08-15-bias-continued.md Co-authored-by: Sam Lindsay --- docs/blog/posts/2024-08-15-bias-continued.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/blog/posts/2024-08-15-bias-continued.md b/docs/blog/posts/2024-08-15-bias-continued.md index 2ae97dc39c..c5819ad656 100644 --- a/docs/blog/posts/2024-08-15-bias-continued.md +++ b/docs/blog/posts/2024-08-15-bias-continued.md @@ -55,7 +55,7 @@ Now that you have records to compare and a trained model, the next stage is to g It's also useful to examine each comparison to see which features impact the match probability the most. In Splink, you can use a [waterfall chart](https://moj-analytical-services.github.io/splink/charts/waterfall_chart.html) for this. This will help you identify if any weights are too predictive or not predictive enough based on your hypothesis. -Some factor weightings might seem off for your hypothesis but be reasonable for the [overall model](#2-train-and-investigate-model). They might not be _wrong_ per se, but if they create issues in specific scenarios, they will **introduce bias into the pipeline**. +Some match weights might seem off for your hypothesis but be reasonable for the [overall model](#2-train-and-investigate-model). They might not be _wrong_ per se, but if they create issues in specific scenarios, they will **introduce bias into the pipeline**. ## 4. Identify bias mitigations

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small edits --- docs/blog/posts/2024-08-15-sd.md | 14 ++++++-------- 1 file changed, 6 insertions(+), 8 deletions(-) diff --git a/docs/blog/posts/2024-08-15-sd.md b/docs/blog/posts/2024-08-15-sd.md index 57ae7cbdd6..2085d2f5b8 100644 --- a/docs/blog/posts/2024-08-15-sd.md +++ b/docs/blog/posts/2024-08-15-sd.md @@ -22,19 +22,17 @@ To achieve this, a common performance evaluation method using clerical review wa ## ⏩ Recent developments -Building upon these challenges, it was determined that the output of a linkage pipeline (linked data) shouldn’t be used to _detect_ bias within a pipeline. Instead, it can be used to understand the _impact_ of any detected bias. +Building upon these challenges, it was determined that the output of a linkage pipeline (linked data) shouldn’t be used to _detect_ bias within a pipeline. Instead, it can be used to understand the _impact_ of any detected bias. -Bias detection, which needs to come before assessing its impact, should be a key part of understanding any linkage pipeline. This means using a bias hypothesis to shape how the model is interpreted. Therefore, this blog will explain an approach for detecting bias in an existing data linking pipeline which has a pre-trained model. There is also a [supplementary notebook]() which demonstrates how this process can work practically using Splink. - -The approach is broken down into 5 key steps: +Detecting bias should be guided by a hypothesis to form a key part of understanding any data linkage pipeline. This blog proposes an approach to bias detection, broken down into 5 key steps: ![Image 1](./img/bias_investigation_steps.png) -These steps will be explained to show how this bias detection process can be used with any data linkage pipeline... +These steps will be explained to show how you can apply this bias detection process to any data linkage pipeline. There's also a [supplementary notebook]() that demonstrates how to do this in practice using Splink. ## 1. Generate synthetic records -This process builds a neutral foundation for bias detection by creating specific records to evaluate the pre-trained model's results. While the process of generating these records can vary, it's crucial to keep it controlled. Automatically generating large amounts of data without understanding the content can lead to issues similar to using real production data. +This process builds a neutral foundation for bias detection by creating specific records to evaluate a pre-trained model's results. While the process of generating these records can vary, it's crucial to keep it controlled. Automatically generating large amounts of data without understanding the content can lead to issues similar to using real production data. Instead of focussing on the number of synthetic records, the key is ensuring each has a clear purpose that is relevant to the hypothesis. Additionally, including records that capture potential downstream effects is useful, as they provide important context for the next steps. @@ -54,7 +52,7 @@ These model parameters will be relevant to the hypothesis, and it's useful to st ## 3. Perform and evaluate linkage -Instead of dumping the every record into the model, it’s better to manually decide which to compare. This lets you focus on the scenarios you care about, rather than sifting through every possible combination. You'll generate match probabilities for each comparison and compare them to the pipeline's thresholds to see which records link. +Instead of dumping the every record into the model, it’s better to manually decide which to compare. This lets you focus on the scenarios you care about, rather than sifting through every possible combination. You'll generate match probabilities for the synthetic record comparisons using the pre-trained model, comparing results to the pipeline's threshold to see which records link. It’s also useful to examine each comparison to see which features impact the match probability the most. In Splink, you can use a [waterfall chart](https://moj-analytical-services.github.io/splink/charts/waterfall_chart.html) for this. This will help you identify if any weights are too predictive or not predictive enough based on your hypothesis. @@ -93,4 +91,4 @@ This approach doesn’t aim to make statements about the _impact_ of the detecte Therefore, the goal here is to better understand bias. This is done by highlighting specific scenarios, examining how the model handles them, and introducing mitigations directly. This approach allows for more transparent statements about bias during the development of a linkage pipeline. -We’ve created a simple demonstration of this process using Splink — please check it out and let us know your feedback! \ No newline at end of file +If you have any ideas or feedback on this process or how it's working in the notebook - **please let us know!** \ No newline at end of file From f7e809812b10ab35e7b03917dffe63c8e9a48d53 Mon Sep 17 00:00:00 2001 From: Ross Kennedy Date: Tue, 17 Sep 2024 14:59:19 +0100 Subject: [PATCH 07/14] add draft complementary notebook --- docs/demos/examples/duckdb/bias_eval.ipynb | 1009 ++++++++++++++++++++ docs/demos/examples/examples_index.md | 4 + mkdocs.yml | 1 + 3 files changed, 1014 insertions(+) create mode 100644 docs/demos/examples/duckdb/bias_eval.ipynb diff --git a/docs/demos/examples/duckdb/bias_eval.ipynb b/docs/demos/examples/duckdb/bias_eval.ipynb new file mode 100644 index 0000000000..ebc0b3a981 --- /dev/null +++ b/docs/demos/examples/duckdb/bias_eval.ipynb @@ -0,0 +1,1009 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### **Bias detection**\n", + "\n", + "This notebook will guide you through a 5-step process for evaluating bias in a data linking pipeline. It offers an isolated approach to **bias detection**, exploring potential mitigation options, before drawing conclusions about the existence of bias in the pipeline.\n", + "\n", + "This approach depends on users having pre-developed hypotheses about bias, based on a thorough understanding of the input data and model design. We’ll illustrate the approach with an example hypothesis, using Splink to show how each step can be practically applied.\n", + "\n", + "Consider this hypothesis:\n", + "\n", + "_I’m working with a 50,000-row dataset in which people appear multiple times over their lives. Because their information changes over time, records relating to the same individuals may not always be identical. I suspect that these changes could negatively impact linkage performance for those who update their details. Since women in this dataset are more likely to change their surname and postcode than men, this negative impact would disproportionately affect women, leading to a gender bias._\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### **1. Generate synthetic data**\n", + "\n", + "Since the hypothesis deals with changes in surnames and addresses, the synthetic data should include multiple records for the same person with these variations:\n", + "\n", + "The synthetic data should also match the structure and format of the production data used to train the model." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "synthetic_base_raw = [\n", + " {\"unique_id\": 1, \"person_id\": 1, \"first_name\": \"sarah\", \"surname\": \"brown\", \"dob\": \"1862-07-11\", \"birth_place\": \"london\", \"postcode_fake\": \"ba12 0ay\", \"gender\": \"female\", \"occupation\": \"politician\"}\n", + "]\n", + "\n", + "synthetic_comparison_raw = [\n", + " {\"unique_id\": 2, \"person_id\": 1, \"first_name\": \"sarah\", \"surname\": \"brown\", \"dob\": \"1862-07-11\", \"birth_place\": \"london\", \"postcode_fake\": \"ba12 0ay\", \"gender\": \"female\", \"occupation\": \"politician\"},\n", + " {\"unique_id\": 3, \"person_id\": 1, \"first_name\": \"sarah\", \"surname\": \"doyle\", \"dob\": \"1862-07-11\", \"birth_place\": \"london\", \"postcode_fake\": \"ba12 0ay\", \"gender\": \"female\", \"occupation\": \"politician\"},\n", + " {\"unique_id\": 4, \"person_id\": 1, \"first_name\": \"sarah\", \"surname\": \"brown\", \"dob\": \"1862-07-11\", \"birth_place\": \"london\", \"postcode_fake\": \"tf3 2ng\", \"gender\": \"female\", \"occupation\": \"politician\"},\n", + " {\"unique_id\": 5, \"person_id\": 1, \"first_name\": \"sarah\", \"surname\": \"doyle\", \"dob\": \"1862-07-11\", \"birth_place\": \"london\", \"postcode_fake\": \"tf3 2ng\", \"gender\": \"female\", \"occupation\": \"politician\"},\n", + " {\"unique_id\": 6, \"person_id\": 2, \"first_name\": \"jane\", \"surname\": \"brown\", \"dob\": \"1860-01-01\", \"birth_place\": \"london\", \"postcode_fake\": \"ba12 0ay\", \"gender\": \"female\", \"occupation\": \"artist\"}\n", + "]\n", + "\n", + "synthetic_base_df = pd.DataFrame(synthetic_base_raw)\n", + "synthetic_comparison_df = pd.DataFrame(synthetic_comparison_raw)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

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