Fair-by-design explainable models for prediction of recidivism

By Eduardo Almeida Soares, Plamen Parvanov Angelov. 2019

25 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Safety Research, Sociology and Political Science, Safety, Robustness & Interpretability, AI Ethics, Fairness & Society

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Citations in the corpus

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (2019, neutral): “Another example is [399] , where the authors propose a fair-by-design approach in order to develop ML models that jointly have less bias and include as explanations human comprehensible rules.”
Towards Deep Machine Reasoning: a Prototype-based Deep Neural Network with Decision Tree Inference (2020, background): “The power of prototype-based approaches stems from the fact that they are explainable-by-design [9], easy to understand by the users because they represent samples of the training data, e.”
Explainable artificial intelligence: an analytical review (2021, background): “As discussed by Soares and Angelov (2019), prototype-based algorithms can be a solution to reduce bias and favor fairness as one can check and balance the prototypes generated to guarantee a fairer decision.”