Fair-by-design explainable models for prediction of recidivism
This addresses fairness and interpretability issues in criminal justice risk assessments, which is an incremental improvement in a domain-specific context.
The paper tackles the problem of potential discriminatory bias in recidivism prediction models by proposing a fair-by-design approach that reduces bias and provides human-interpretable rules for specialists.
Recidivism prediction provides decision makers with an assessment of the likelihood that a criminal defendant will reoffend that can be used in pre-trial decision-making. It can also be used for prediction of locations where crimes most occur, profiles that are more likely to commit violent crimes. While such instruments are gaining increasing popularity, their use is controversial as they may present potential discriminatory bias in the risk assessment. In this paper we propose a new fair-by-design approach to predict recidivism. It is prototype-based, learns locally and extracts empirically the data distribution. The results show that the proposed method is able to reduce the bias and provide human interpretable rules to assist specialists in the explanation of the given results.