Resources for AI Fairness


The Joint Statistics Meetings had a great talk on Fairness in Artifical Intelligence (AI). The speaker, Sherrie Rose, documented instances where AI has had a discriminatory effect, the wide ranges of causes of this, and how to fix things. I looked up some of the references and resources that she mentioned in her talk and list them below.

The best place to start is Fairness, Accountability, and Transparency in Machine Learning, a group that organizes annual conferences on this topic. They have a very nice resource list. Also quite helpful is a working group within the American Statistical Association, the Justice, Equity, Diversity, and Inclusion (JEDI) Outreach Group.

Statistics Canada has a couple of valuable resources:

Stories in the news media about this topic include

There are various pre-prints and peer-reviewed publications that offer technical explanations of the problem and propose some solutions.