Studying Up: Reorienting the field of algorithmic fairness around issues of power

On January 28, 2020, I presented a paper at the ACM FAccT* conference. Below is the write-up of that presentation. You can find the paper here: https://dl.acm.org/doi/abs/10.1145/3351095.3372859

Einstein once said that if he had an hour to solve a problem and his life depended on the solution, he would spend the first 55 minutes determining the proper question to ask.

As academics, so much of our power lies in how we frame the problems we aim to solve, in formulating the right question.

Yet, the academic community dedicated to the pursuit of “fair” algorithmic systems has not taken enough time to develop the right set of questions in pursuit of this goal. In spite of our best efforts, data scientists still lack the methodological and conceptual tools necessary to grapple with key epistemological and normative aspects of their work. As a result, data scientists tend to uncritically inherit dominant modes of seeing and understanding the world when conceiving of their projects. In doing so, they reproduce ideas which normalize social hierarchies and legitimize violence against marginalized groups.

In our paper we challenge data scientists to move beyond these default modes of operating in favor of “studying up.”