Fair-by-design explainable models for prediction of recidivism

Almeida Soares, Eduardo and Angelov, Plamen (2019) Fair-by-design explainable models for prediction of recidivism. Arxiv.

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Abstract

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.

Item Type:
Other
ID Code:
138384
Deposited By:
Deposited On:
29 Oct 2019 15:10
Refereed?:
No
Published?:
Published
Last Modified:
27 Nov 2020 07:02