On the Fairness ROAD: Robust Optimization for Adversarial Debiasing

In the field of algorithmic fairness, significant attention has been put on group fairness criteria, such as Demographic Parity and Equalized Odds. Nevertheless, these objectives, measured as global averages, have raised concerns about persistent local disparities between sensitive groups. In this work, we address the problem of local fairness, which ensures that the predictor is unbiased not only in terms of expectations over the whole population, but also within any subregion of the feature space, unknown at training time. To enforce this objective, we introduce ROAD, a novel approach that leverages the Distributionally Robust Optimization (DRO) framework within a fair adversarial learning objective, where an adversary tries to infer the sensitive attribute from the predictions. Using an instance-level re-weighting strategy, ROAD is designed to prioritize inputs that are likely to be locally unfair, i.e. where the adversary faces the least difficulty in reconstructing the sensitive attribute. Numerical experiments demonstrate the effectiveness of our method: it achieves Pareto dominance with respect to local fairness and accuracy for a given global fairness level across three standard datasets, and also enhances fairness generalization under distribution shift.Comment: 23 pages, 10 figure

Réduire les biais indésirables en apprentissage automatique par atténuation adverse

Ces dernières années, on a assisté à une augmentation spectaculaire de l’intérêt académique et sociétal pour l’apprentissage automatique équitable. En conséquence, des travaux significatifs ont été réalisés pour inclure des contraintes d’équité dans les algorithmes d’apprentissage automatique. Le but principal est de s’assurer que les prédictions des modèles ne dépendent d’aucun attribut sensible comme le genre ou l’origine d’une personne par exemple. Bien que cette notion d’indépendance soit incontestable dans un contexte général, elle peut théoriquement être définie de manière totalement différente selon la façon dont on voit l’équité. Par conséquent, de nombreux articles récents abordent ce défi en utilisant leurs "propres" objectifs et notions d’équité. Les objectifs sont catégorisés en deux familles différentes : L’équité individuelle et l’équité de groupe. Cette thèse donne d’une part, une vue d’ensemble des méthodologies appliquées dans ces différentes familles afin d’encourager les bonnes pratiques. Ensuite, nous identifions et complétons les lacunes en présentant de nouvelles métriques et des algorithmes de machine learning équitables qui sont plus appropriés pour des contextes spécifiques.The past few years have seen a dramatic rise of academic and societal interest in fair machine learning. As a result, significant work has been done to include fairness constraints in the training objective of machine learning algorithms. Its primary purpose is to ensure that model predictions do not depend on any sensitive attribute as gender or race, for example. Although this notion of independence is incontestable in a general context, it can theoretically be defined in many different ways depending on how one sees fairness. As a result, many recent papers tackle this challenge by using their "own" objectives and notions of fairness. Objectives can be categorized in two different families: Individual and Group fairness. This thesis gives an overview of the methodologies applied in these different families in order to encourage good practices. Then, we identify and complete gaps by presenting new metrics and new Fair-ML algorithms that are more appropriate for specific contexts

Réduire les biais indésirables en apprentissage automatique par atténuation adverse

The past few years have seen a dramatic rise of academic and societal interest in fair machine learning. As a result, significant work has been done to include fairness constraints in the training objective of machine learning algorithms. Its primary purpose is to ensure that model predictions do not depend on any sensitive attribute as gender or race, for example. Although this notion of independence is incontestable in a general context, it can theoretically be defined in many different ways depending on how one sees fairness. As a result, many recent papers tackle this challenge by using their "own" objectives and notions of fairness. Objectives can be categorized in two different families: Individual and Group fairness. This thesis gives an overview of the methodologies applied in these different families in order to encourage good practices. Then, we identify and complete gaps by presenting new metrics and new Fair-ML algorithms that are more appropriate for specific contexts.Ces dernières années, on a assisté à une augmentation spectaculaire de l’intérêt académique et sociétal pour l’apprentissage automatique équitable. En conséquence, des travaux significatifs ont été réalisés pour inclure des contraintes d’équité dans les algorithmes d’apprentissage automatique. Le but principal est de s’assurer que les prédictions des modèles ne dépendent d’aucun attribut sensible comme le genre ou l’origine d’une personne par exemple. Bien que cette notion d’indépendance soit incontestable dans un contexte général, elle peut théoriquement être définie de manière totalement différente selon la façon dont on voit l’équité. Par conséquent, de nombreux articles récents abordent ce défi en utilisant leurs "propres" objectifs et notions d’équité. Les objectifs sont catégorisés en deux familles différentes : L’équité individuelle et l’équité de groupe. Cette thèse donne d’une part, une vue d’ensemble des méthodologies appliquées dans ces différentes familles afin d’encourager les bonnes pratiques. Ensuite, nous identifions et complétons les lacunes en présentant de nouvelles métriques et des algorithmes de machine learning équitables qui sont plus appropriés pour des contextes spécifiques

A fair pricing model via adversarial learning

At the core of insurance business lies classification between risky and non-risky insureds, actuarial fairness meaning that risky insureds should contribute more and pay a higher premium than non-risky or less-risky ones. Actuaries, therefore, use econometric or machine learning techniques to classify, but the distinction between a fair actuarial classification and "discrimination" is subtle. For this reason, there is a growing interest about fairness and discrimination in the actuarial community Lindholm, Richman, Tsanakas, and Wuthrich (2022). Presumably, non-sensitive characteristics can serve as substitutes or proxies for protected attributes. For example, the color and model of a car, combined with the driver's occupation, may lead to an undesirable gender bias in the prediction of car insurance prices. Surprisingly, we will show that debiasing the predictor alone may be insufficient to maintain adequate accuracy (1). Indeed, the traditional pricing model is currently built in a two-stage structure that considers many potentially biased components such as car or geographic risks. We will show that this traditional structure has significant limitations in achieving fairness. For this reason, we have developed a novel pricing model approach. Recently some approaches have Blier-Wong, Cossette, Lamontagne, and Marceau (2021); Wuthrich and Merz (2021) shown the value of autoencoders in pricing. In this paper, we will show that (2) this can be generalized to multiple pricing factors (geographic, car type), (3) it perfectly adapted for a fairness context (since it allows to debias the set of pricing components): We extend this main idea to a general framework in which a single whole pricing model is trained by generating the geographic and car pricing components needed to predict the pure premium while mitigating the unwanted bias according to the desired metric.Comment: 20 pages, 12 figure

Joint modeling of claim frequencies and behavioral signals in motor insurance

Telematics devices installed in insured vehicles provide actuaries with new risk factors, such as the time of the day, average speeds, and other driving habits. This paper extends the multivariate mixed model describing the joint dynamics of telematics data and claim frequencies proposed by Denuit et al. (2019a) by allowing for signals with various formats, not necessarily integer-valued, and by replacing the estimation procedure with the Expected Conditional Maximization algorithm. A numerical study performed on a database related to Pay-How-You-Drive, or PHYD motor insurance illustrates the relevance of the proposed approach for practice