Philosophy / Law Track
Sociocultural Diversity in Machine Learning: Lessons from Philosophy, Psychology and Organizational
Sociocultural diversity is a key value in democratic societies both for reasons of equity and political legitimacy and because of its ramifications for group performance. As a result, researchers in philosophy, psychology, and social and organizational sciences have worked to understand diversity’s varied meanings, develop appropriate measures for quantifying diversity (in some specific sense), and specifying pathways by which diversity can be functionally beneficial to groups such as deliberative mini-publics, design teams, and scientific communities. Recently, there has also been a surge of interest in sociocultural diversity in machine learning (ML) research, with researchers proposing (i) diversity as a design desideratum in the construction pipeline of sociotechnical ML systems and (ii) team diversity as an organizational ideal for the composition of ML research and development teams. With respect to (i), researchers have proposed various measures of diversity, developed methods for satisfying these measures via modifications in data processing and learning, and examined the interaction between diversity, accuracy, and fairness. With respect to (ii), there have been claims about the benefits of team diversity in countering biases in ML systems, with more recent efforts aiming to empirically test these purported benefits. Currently, however, there is a gap between discussions of measures and benefits of diversity in ML, on the one hand, and the philosophical, psychological and organizational research on the underlying concepts of diversity and the precise mechanisms of its functional benefits, on the other. This gap is problematic because different concepts of diversity are based on distinct sets of cognitive, ethical, and political rationales that should inform how we measure diversity in a given context and why. Similarly, lack of specificity about the precise mechanisms underpinning diversity’s potential benefits can result in espousing uninformative or easily falsifiable generalities, invalid experimental designs, and illicit inferences from experimental results. This tutorial will bridge this gap from both angles—concepts and consequences. The first part will focus on discussions of concepts and consequences of sociocultural diversity, in philosophy, psychology, and social and organizational sciences. The second part will situate this understanding in and draw its implications for the discussions of sociocultural diversity in ML.