Added by: Tomasz Zyglewicz, Shannon Brick, Michael GreerIntroduction: Let’s set the stage. In 2016, ProPublica released a ground-breaking investigation called Machine Bias. You’ve probably heard of it. They examined a criminal risk prediction tool that’s used across the country. These are tools that claim to predict the likelihood that a defendant will reoffend if released, and they are used to inform bail and parole decisions.
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.
Narayanan, Arvind. The Limits of the Quantitative Approach to Discrimination
2022, James Baldwin Lecture Series
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