Below is a sampling books and course textbooks about AI/ML Ethics, drawn from course syllabi, research, and other sources. New books are frequently being published; please write us an email or create a Github issue witha any additional suggestions!
AI Ethics (MIT Press Essential Knowledge series)
- AI Ethics (MIT Press Essential Knowledge series), Paperback, Mark Coeckelbergh (2020)
- A modern perspective on AI ethics written by a philopher, citing several recent examples of AI ethics quandaries. May be interest to federal workers becauase it contains a discussion of policy proposals and issues with which policymakers might content
Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy Paperback – September 5, 2017 by Cathy O'Neil (Author), Crown Publishing
Blurb from Amazon: "We live in the age of the algorithm. Increasingly, the decisions that affect our lives—where we go to school, whether we can get a job or a loan, how much we pay for health insurance—are being made not by humans, but by machines. In theory, this should lead to greater fairness: Everyone is judged according to the same rules. But as mathematician and data scientist Cathy O’Neil reveals, the mathematical models being used today are unregulated and uncontestable, even when they’re wrong. Most troubling, they reinforce discrimination—propping up the lucky, punishing the downtrodden, and undermining our democracy in the process. Welcome to the dark side of Big Data."
Change Through Data: A Data Analytics Training Program for Government Employees https://hdsr.mitpress.mit.edu/pub/0mb0zzlc/release/5
Bias and Fairness (in Machine Learning) Book Chapter. Kit T. Rodolfa, Pedro Saleiro, Rayid Ghani. In Big Data and Social Science: A Practical Guide to Methods and Tools. Chapman and Hall/CRC Press, 2020
Machine Learning (Book Chapter). Rayid Ghani and Malte Schierholz. In Big Data and Social Science: A Practical Guide to Methods and Tools. Chapman and Hall/CRC Press, 2016.
Algorithms of Oppression How Search Engines Reinforce Racism by Safiya Umoja Noble
-
10718, 94889 Data Analysis / Machine Learning for Public Policy Lab Carnegie Mellon University, 2020 edition
- This course, which assumes prior exposure to machine learning and proficiency with Python, is rich with material for those trying to get started building end-to-end machine learning pipelines for government applications, and who would like formal technical exposure to data analysis and machine learning work with ethical considerations. The course features a template for field projects suitable for training programs mini-courses. Lessons of interest to those interested in AI/ML ethics in civic tech/policy settings are the modules on Bias and Fairness, Model Explainability/Interpretability, and Field Validation.
-
RES.EC-001 Exploring Fairness in Machine Learning for International Developments MIT, Spring 2020
- Lightly technical short course offered in full through MIT's free Open Courseware resource, geared toward those interested in implementable applied methods for AI/ML ethics. Notable resources are a series of video lectures, and full case studies featuring data and technical approaches, including: Mitigating Gender Bias on the UCI Adult Database, a case study on Pulmonary Health, and a case study on Identifying and Mitigating Unintended Demographic Bias in Machine Learning for NLP.
Responsible Artificial Intelligence: How to Develop and Use AI in a Responsible Way (Artificial Intelligence: Foundations, Theory, and Algorithms) 1st ed. 2019 Edition Virginia Dignum, Springer
Blurb from Amazon: "In this book, the author examines the ethical implications of Artificial Intelligence systems as they integrate and replace traditional social structures in new sociocognitive-technological environments. She discusses issues related to the integrity of researchers, technologists, and manufacturers as they design, construct, use, and manage artificially intelligent systems; formalisms for reasoning about moral decisions as part of the behavior of artificial autonomous systems such as agents and robots; and design methodologies for social agents based on societal, moral, and legal values.
Throughout the book the author discusses related work, conscious of both classical, philosophical treatments of ethical issues and the implications in modern, algorithmic systems, and she combines regular references and footnotes with suggestions for further reading. This short overview is suitable for undergraduate students, in both technical and non-technical courses, and for interested and concerned researchers, practitioners, and citizens."
Kevin Murphy's model-based approach to machine learning (with pseudocode): Machine Learning, a Probabilistic Perspective https://geni.us/5h7oHK
O'Reilly's Hands-On Machine Learning, which introduces users to critical tools and frameworks like Keras, Scikit-Learn, and Tensorflow: https://geni.us/DkXs
Shalev-Schwartz and Ben-David, Understanding Machine Learning: A math-intensive introduction to theories and algorithms, and topics like convexity: https://geni.us/q7xFj
-
Data c104 / History c184d Human Contexts and Ethics of Data UC Berkeley Fall 2020
- An interesting course jointly offering by the Data Science and History programs at the University of California at Berkeley. This course is of particular relevance to those working in civic tech contexts, giving a historical overview of ethics of institutions as it relates to data use, touching on timely issues ranging from predictive policing to elections, and supplying modern real-world illustrations of the importance of responsible data use by drawing on themes from the field of Science, Technology, and Society.
-
USF Applied Data Ethics Practical Data Ethics FastAI/University of San Francisco 2020
- As the name suggests, this is a practical course featuring immediate applications of data ethics, designed to be accessible to diverse audiences and thus requiring no formal prerequsites. Of particular value are Lesson 2, which defines metrics and bias, approaches to reduce bias, and real-life complexities of "doing fair AI/ML," and Lesson 3, Ethical Foundations & Practical Tools, which features work on Mitchell et al's Model Cards, and resources on inclusive technology policy design.
-
USD AI and the Law Artificial Intelligence and the Law University of San Diego School of Law 2020
- This course explores the legal implications of the proliferation of AI/ML technologies. The reading list offered in teh syllabus features papers on useful topics like Black Box AI, Discrimination, Manipulation and Due Process, and AI and Tort and Criminal Liability, which could offer a legal dimension of AI/Ml useful for those trying to delve into the social impact and policy implications of these technologies.