README.md

Gender Bias Measurement of Information Retrieval

This repository contains the resources and code for measuring gender bias in the result list of Information Retrieval models, as explained in the paper: https://arxiv.org/abs/2005.00372

Resources:

• resources/queries_gender_annotated.csv: Annotation results of the related gender of queries.
• resources/wordlist_genderspecific.txt: List of gender specific words.

Code

Step 1 : calculating bias for the collection's documents

Follow the code in step1_calculate_bias_documents.ipynb for calculating the gender bias of each document in collection. At the beginning of the notebook, set collection_path to the path of the collection with TSV format. For MS MARCO collection, this should refer to the collection.tsv file. The code does not apply any particular pre-processing to the text unless converting it to lower case. It is suggested that you apply the required pre-processing before hand.

Step 2 : calculating bias for TREC run files

Using the pre-calculated document biases, step2_calculate_bias_runs.ipynb calculates gender bias scores for each query for the given retrieval run files.

Step 3 : RaB and ARaB metrics

• step3_bias_metrics.ipynb calculates the final gender bias metrics for each experiment.

Reference

The paper will appear in the proceedings of SIGIR 2020. Reference to the arXiv version:

@inproceedings{rekabsaz2020neural,
    author = {Rekabsaz, Navid and Schedl, Markus}, 
    title = {Do Neural Ranking Models Intensify Gender Bias?}, 
    year = {2020}, 
    doi = {10.1145/3397271.3401280}, 
    booktitle = {Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval}, 
}