  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.
