Tag Archives: Singapore

Modeling and Understanding Indirect Discrimination in Algorithmic Fairness

In a couple of days, I will give a talk on “Modeling and Understanding Indirect Discrimination in Algorithmic Fairness” at Singapore campus – ESSEC Asia-Pacific. The abstract is

Observed disparities between groups in algorithmic decisions (whether in hiring, credit approval, or risk prediction) do not necessarily imply direct discrimination. They may also stem from legitimate differences in the distribution of explanatory attributes. Understanding and quantifying which components of these gaps are “explained” versus those that reflect direct or indirect discrimination lies at the core of modern causal approaches to algorithmic fairness. This talk will begin with an accessible introduction to group-gap decomposition, building on the classical Kitagawa–Oaxaca–Blinder econometric framework. This approach separates differences attributable to observable characteristics from residual components that may signal discriminatory effects. The second part will introduce recent developments leveraging optimal transport to construct individual-level counterfactuals, enabling estimation of direct and indirect causal effects for each observation. In particular, we will show how sequential transport mappings aligned with a causal graph can disentangle pathways and quantify the contribution of each mediator. This methodology overcomes limitations of traditional linear models, introduced by Kitagawa, Oaxaca and Blinder, provides interpretable counterfactuals, and is well suited to complex empirical settings. The presentation will combine intuitive motivation, illustrative examples, and recent research insights, with the goal of making these tools accessible and useful to researchers in management science, applied economics, and data science.

Talk at NTU (Nanyang) in Singapore

Tomorrow, I will be at Nanyang Technological University to give a talk at an internal seminar, “Fairness and discrimination in insurance

What’s unique about insurance is that even statistical discrimination, which by definition is devoid of malicious intent, poses significant challenges. Because, on the one hand, policymakers would like insurers to treat their policyholders equally, without discrimination based on race, gender, age or other characteristics, even if it could make (statistical) sense to (indirectly) discriminate. On the other hand, at the core of actuaries’ activities lies discrimination, between risky and non-risky policyholders. And this risk is often statistically correlated with sensitive characteristics that regulation would like to prohibit insurers from taking into account. The analysis of possible discrimination in decision rules, whether human or algorithmic, is an old subject. Most of the concepts date back at least to the 50s, but recent developments in artificial intelligence have brought these issues back into the spotlight. Massive data facilitate statistical or proxy discrimination, and black-box algorithms do not facilitate understanding. Not to mention the various regulations that make it difficult to collect sensitive information, and ultimately test whether decisions can be discriminated against, especially indirectly.

The talk is based on the textbook Insurance, Biases, Discrimination and Fairness, as well as recent papers, arXiv:2511.11294 (AAAI’26), arXiv:2408.03425 (AAAI’25), arXiv:2309.06627 (AAAI’24) and arXiv:2306.12912  (ECML’24).

Buzy week in Singapore

It has been a buzy week at the 40th Annual AAAI Conference on Artificial Intelligence, here in Singapore where Bertille Tierny and François Hu will give talks (in the “main track”, in the “student track”, in a workshop) to present our recent work, “Decomposing Direct and Indirect Biases in Linear Models under Demographic Parity Constraint“. More to come very soon…

On my way to Singapore

By the end of the week, I will be in Singapore. I plan to spend some time at the 40th Annual AAAI Conference on Artificial Intelligence, where Bertille Tierny and François Hu will give talks (in the “main track” and in the “student track”) to present our recent work, “Decomposing Direct and Indirect Biases in Linear Models under Demographic Parity Constraint“.

Then I will spend two weeks, invited at ESSEC Asia-Pacific, invited by Pierre Alquier. A couple of talks are also scheduled.