Titlebook: Insurance, Biases, Discrimination and Fairness; Arthur Charpentier Book 2024 The Editor(s) (if applicable) and The Author(s), under exclus

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Introduction,he subject of an extensive body of literature over the past decades. But dealing with discrimination in insurance is fundamentally an ill-defined, unsolvable problem. Nevertheless, we try to connect the dots, to explain different perspectives, going back to the legal, philosophical, and economic app

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Filibuster

Models: Interpretability, Accuracy, and Calibrationainability of models and algorithms, presenting different tools that could help us to understand “why” the predicted outcome of the model is the one we got. Then, we will discuss accuracy, which is usually the ultimate target of most machine-learning techniques. But as we see, the most important con

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In-processinghe model is required to satisfy constraints, a natural idea is to add a penalty term in the objective function. The idea of “in-processing” is to get a trade-off between accuracy and fairness. As previously, we present that approach to some datasets.