Fairness Is More Than Algorithms: Racial Disparities in Time-to-Recidivism

2025-04-25

AI Summary

This paper investigates the persistence of racial disparities in recidivism timing within criminal justice, emphasizing non-algorithmic structural factors—such as housing and employment access—rather than attributing disparities solely to algorithmic bias. Method: We propose a multi-stage causal framework with time-to-recidivism as a continuous outcome, introducing the novel concept of “counterfactual racial disparity.” Integrating Cox proportional hazards modeling with counterfactual reasoning, our approach enables empirically testable identification and separation of algorithmic bias from socioeconomic and structural determinants using observational data. Contribution/Results: Applying the framework to COMPAS data, we find no statistically significant racial disparity in short-term recidivism, but robust disparities emerge over longer horizons—particularly among individuals classified as low-risk—indicating that structural disadvantages cumulatively exacerbate inequality over time. Our framework establishes a new causal paradigm for diagnosing, understanding, and intervening in systemic judicial inequities.

Technology Category

• Computer Vision: Bias, Fairness & Privacy
• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Machine Learning: Ethics, Bias, and Fairness

Application Category

• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Racial disparities in recidivism remain a persistent challenge within the criminal justice system, increasingly exacerbated by the adoption of algorithmic risk assessment tools. Past works have primarily focused on bias induced by these tools, treating recidivism as a binary outcome. Limited attention has been given to non-algorithmic factors (including socioeconomic ones) in driving racial disparities from a systemic perspective. To that end, this work presents a multi-stage causal framework to investigate the advent and extent of disparities by considering time-to-recidivism rather than a simple binary outcome. The framework captures interactions among races, the algorithm, and contextual factors. This work introduces the notion of counterfactual racial disparity and offers a formal test using survival analysis that can be conducted with observational data to assess if differences in recidivism arise from algorithmic bias, contextual factors, or their interplay. In particular, it is formally established that if sufficient statistical evidence for differences across racial groups is observed, it would support rejecting the null hypothesis that non-algorithmic factors (including socioeconomic ones) do not affect recidivism. An empirical study applying this framework to the COMPAS dataset reveals that short-term recidivism patterns do not exhibit racial disparities when controlling for risk scores. However, statistically significant disparities emerge with longer follow-up periods, particularly for low-risk groups. This suggests that factors beyond algorithmic scores, possibly structural disparities in housing, employment, and social support, may accumulate and exacerbate recidivism risks over time. This underscores the need for policy interventions extending beyond algorithmic improvements to address broader influences on recidivism trajectories.

Problem

• Investigates racial disparities in time-to-recidivism beyond algorithmic bias
• Examines systemic factors like socioeconomic conditions affecting recidivism outcomes
• Proposes causal framework to test algorithmic and contextual disparity sources

Innovation

• Multi-stage causal framework for recidivism disparities
• Counterfactual racial disparity with survival analysis
• Empirical validation using COMPAS dataset