The Values Encoded in Machine Learning Research
Best paper at FAccT, 2022
It is critical that we question vague conceptions of machine learning research as value-neutral or universally beneficial, and investigate what specific values the field is advancing. In this paper, we annotate one hundred highly cited machine learning papers. We find that few of the papers justify how their project connects to a societal need (15%) and far fewer discuss negative potential (1%). We find that the papers most frequently justify and assess themselves based on Performance, Generalization, Quantitative evidence, and Efficiency; and these are being defined in ways that centralize power. Finally, we find increasingly close ties between these highly cited papers and tech companies and elite universities.