Who Is a Good Data Scientist? A Reply to Curzer and Epstein

A central distinction in Curzer and Epstein (2022) is the one between “protect the disadvantaged” and “protect the data”. This can open up discussions about the relationship between ethics and epistemology in the practice of science. Focusing on the disadvantaged to the exclusion of good scientific practices, Curzer and Epstein argue, can harm everyone impacted by medical science, including the disadvantaged. For this reason, they propose that “ethical data scientists should strive for accurate data and scientifically sound data analysis” (2022, p 2) with attention to minimizing data processing errors, bias, and outside influence, and that includes identifying errors caused by tendencies to neglect disadvantaged and historically underrepresented communities and groups. While we agree with several points made by Curzer and Epstein, we also have three main points of concern: 1 Microethics and the “Protect the Disadvantaged” Approach; 2 Ethics and Epistemology, or Science and Values; 3 The Social Context of Data Science.