Although computer scientists are eager to help address social problems, the field faces a growing awareness that many well-intentioned applications of algorithms in social contexts have led to significant harm. We argue that addressing this gap between the field's desire to do good and the harmful impacts of many of its interventions requires looking to the epistemic and methodological underpinnings of algorithms. We diagnose the dominant mode of algorithmic reasoning as "algorithmic formalism" and describe how formalist orientations lead to harmful algorithmic interventions. Addressing these harms requires pursuing a new mode of algorithmic thinking that is attentive to the internal limits of algorithms and to the social concerns that fall beyond the bounds of algorithmic formalism. To understand what a methodological evolution beyond formalism looks like and what it may achieve, we turn to the twentieth century evolution in American legal thought from legal formalism to legal realism. Drawing on the lessons of legal realism, we propose a new mode of algorithmic thinking---"algorithmic realism"---that provides tools for computer scientists to account for the realities of social life and of algorithmic impacts. These realist approaches, although not foolproof, will better equip computer scientists to reduce algorithmic harms and to reason well about doing good.
"Algorithmic Realism: Expanding the Boundaries of Algorithmic Thought"
Areas of Interest
ACM Conference on Fairness, Accountability, and Transparency (ACM FAT*)