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

This research highlights persistent racial disparities in recidivism, moving beyond traditional binary outcome analysis to examine 'time-to-recidivism.' It scrutinizes the role of both risk assessment algorithms and non-algorithmic contextual factors in these disparities. The study introduces a multi-stage causal framework and the concept of 'interventional racial parity' to understand the complex interactions driving these outcomes.

Addressing racial disparities in outcomes such as recidivism is critical for maintaining social equity and public trust in justice systems. This research contributes to a deeper understanding of the complex interplay between algorithmic predictions, human decision-making, and societal factors, which can inform the design of more equitable and effective risk assessment and intervention strategies.

Racial disparities in recidivism present an ongoing challenge.

Forward consideration, not a verified fact.