Fairness Deconstructed: A Sociotechnical View of 'Fair' Algorithms in Criminal Justice

TL;DR

This paper critiques current fairness approaches in criminal justice algorithms, emphasizing the importance of social context and epistemological issues in understanding and addressing structural biases.

Contribution

It highlights the gap between fairness theory and practice, advocating for sociotechnical perspectives and root cause analysis over purely algorithmic fairness metrics.

Findings

Social context can undermine fairness analyses based solely on AI outputs.

Many fairness methods overlook epistemological issues with crime data.

Calls for using data science to understand structural marginalization.

Abstract

Early studies of risk assessment algorithms used in criminal justice revealed widespread racial biases. In response, machine learning researchers have developed methods for fairness, many of which rely on equalizing empirical metrics across protected attributes. Here, I recall sociotechnical perspectives to delineate the significant gap between fairness in theory and practice, focusing on criminal justice. I (1) illustrate how social context can undermine analyses that are restricted to an AI system's outputs, and (2) argue that much of the fair ML literature fails to account for epistemological issues with underlying crime data. Instead of building AI that reifies power imbalances, like risk assessment algorithms, I ask whether data science can be used to understand the root causes of structural marginalization.