The Values Encoded in Machine Learning Research

Overview: Machine learning is often portrayed as a value-neutral endeavor; even when that is not the exact position taken, it is implicit in how the research is carried out and how the results are communicated. This paper undertakes a qualitative analysis of the top 100 most cited papers from NeurIPS and ICML to uncover some of the most prominent values these papers espouse and how they shape the path forward. As we get a higher proliferation of AI in various aspects of our lives, critical scholars have raised concerns about the negative impacts of these systems on society. Yet, most technical papers published today pay little to no attention to the societal implications of their work. And this is despite emerging requirements like "Broader Impact Statements" that have become mandatory at several conferences. Through the manual analysis of 100 papers, this research surfaces trends that support this position and articulates that machine learning is not value-neutral.