Talk at the ESSEC Risk Seminar

Thursday, I will be at La Défense, in Paris (France), to give a talk at the ESSEC Risk Seminar, entitled Causal Inference and Counterfactuals with Optimal Transport, with Applications in Fairness and Discrimination. Slides are now available, and the talk will be based on same recent papers, starting with Mitigating Discrimination in Insurance with Wasserstein Barycenters (presented last week-end at BIAS 2023) but also Fairness in Multi-Task Learning via Wasserstein Barycenters, and A Sequentially Fair Mechanism for Multiple Sensitive Attributes. There is also the textbook that should appear before the winter.

Melting contestation: insurance fairness and machine learning

Our paper, Melting contestation: insurance fairness and machine learning, with Laurence Barry, is now published (in Ethics and Information Technology).

With their intensive use of data to classify and price risk, insurers have often been confronted with data-related issues of fairness and discrimination. This paper provides a comparative review of discrimination issues raised by traditional statistics versus machine learning in the context of insurance. We first examine historical contestations of insurance classification, showing that it was organized along three types of bias: pure stereotypes, non-causal correlations, or causal effects that a society chooses to protect against, are thus the main sources of dispute. The lens of this typology then allows us to look anew at the potential biases in insurance pricing implied by big data and machine learning, showing that despite utopic claims, social stereotypes continue to plague data, thus threaten to unconsciously reproduce these discriminations in insurance. To counter these effects, algorithmic fairness attempts to define mathematical indicators of non-bias. We argue that this may prove insufficient, since as it assumes the existence of specific protected groups, which could only be made visible through public debate and contestation. These are less likely if the right to explanation is realized through personalized algorithms, which could reinforce the individualized perception of the social that blocks rather than encourages collective mobilization.

Fairness in Multi-Task Learning via Wasserstein Barycenters, at ECML PKDD 2023

Toady, presentation of our paper Fairness in Multi-Task Learning via Wasserstein Barycenters, ECML PKDD, in Torino, by François. Slides are available online (and a poster can be found below)

The paper was actually published in Machine Learning and Knowledge Discovery in Databases: Research Track (295–312), available here.

A Sequentially Fair Mechanism for Multiple Sensitive Attributes

Our paper, A Sequentially Fair Mechanism for Multiple Sensitive Attributes, with François Hu and Philipp Ratz, is now available on Arxiv,

In the standard use case of Algorithmic Fairness, the goal is to eliminate the relationship between a sensitive variable and a corresponding score. Throughout recent years, the scientific community has developed a host of definitions and tools to solve this task, which work well in many practical applications. However, the applicability and effectivity of these tools and definitions becomes less straightfoward in the case of multiple sensitive attributes. To tackle this issue, we propose a sequential framework, which allows to progressively achieve fairness across a set of sensitive features. We accomplish this by leveraging multi-marginal Wasserstein barycenters, which extends the standard notion of Strong Demographic Parity to the case with multiple sensitive characteristics. This method also provides a closed-form solution for the optimal, sequentially fair predictor, permitting a clear interpretation of inter-sensitive feature correlations. Our approach seamlessly extends to approximate fairness, enveloping a framework accommodating the trade-off between risk and unfairness. This extension permits a targeted prioritization of fairness improvements for a specific attribute within a set of sensitive attributes, allowing for a case specific adaptation. A data-driven estimation procedure for the derived solution is developed, and comprehensive numerical experiments are conducted on both synthetic and real datasets. Our empirical findings decisively underscore the practical efficacy of our post-processing approach in fostering fair decision-making.