Diversity Reading List

Comment (from this Blueprint): Cokley and Awad are both psychologists, whose work seeks to redress the wrongs of past injustices against marginalized groups, and who both use quantitative methods to do so. In this article, they sketch some of the historical reasons why members of marginalized groups are sometimes rightly suspicious of the use of quantative techniques. However, they both argue that quantitative methods are not necessarily oppressive, but can be put to good use provided their practioners are committed to social justice. They offer some examples, from their own work, of how this sort of quantitative work can help to further the cause of social justice.

Comment (from this Blueprint): This is a written transcript of the James Baldwin lecture, delivered by the computer scientist Arvind Narayanan, at Princeton in 2022. Narayanan's prior research has examined algorithmic bias and standards of fairness with respect to algorithmic decision making. Here, he engages critically with his own discipline, suggesting that there are serious limits to the sorts of quantitative methods that computer scientists recruit to investigate the potential biases in their own tools. Narayanan acknowledges that in voicing this critique, he is echoing claims by feminist researchers from fields beyond computer science. However, his own arguments, centered as they are on the details of the quantitative methods he is at home with, home in on exactly why these prior criticisms hold up in a way that seeks to speak more persuasively to Narayanan's own peers in computer science and other quantitative fields.