This week, François is in Vancouver, at the 38th Annual AAAI Conference on Artificial Intelligence,
presenting our joint work on Sequentially Fair Mechanism for Multiple Sensitive Attributes,
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.
For our paper, (A Sequentially Fair Mechanism for Multiple Sensitive Attributes) accepted at the 38th Annual AAAI Conference on Artificial Intelligence, in Vancouver, we were supposed to create a 12 minute video.
Of course, Philipp decided to keep some material that was not used for a blooper….
Good news: ou paper, A Sequentially Fair Mechanism for Multiple Sensitive Attributes, written with Philipp Ratz and François Hu will be presented in February in Vancouver, at the 38th Annual AAAI Conference on Artificial Intelligence. For a shorter version, there was a review on montrealethics.ai of the paper last week (as mentioned previously).
Also, last week also, the team launched the equipy python package, with codes used in the paper,
pip install equipy
EquiPy is a Python package implementing sequential fairness on the predicted outputs of Machine Learning models, when dealing with multiple sensitive attributes. This post-processing method progressively achieve fairness accross a set of sensitive features by leveraging multi-marginal Wasserstein barycenters, which extends the standard notion of Strong Demographic Parity to the case with multiple sensitive characteristics. This approach seamlessly extends to approximate fairness, enveloping a framework accommodating the trade-off between performance and unfairness.
(from the left to the right, Agathe, who just joint the PhD program, Suzie, MSc student at ENSAE, with us since May or June, Philipp, PhD student, François, postdoctoral fellow – and Dante, also postdoctoral fellow, in stochastic processes). According to Aristotle (or probably slightly misquoted),
the whole is greater than the sum of the parts
I couldn’t agree more !