50 citations · 151 across the 9 of their papers we have counts for
7 papers · 1 filter
Learning Unbiased Representations via Rényi Minimization
Vincent Grari, Oualid El Hajouji, Sylvain Lamprier +1
In recent years, significant work has been done to include fairness constraints in the training objective of machine learning algorithms. Many state-of the-art algorithms tackle th…
Adversarial Learning for Counterfactual Fairness
Vincent Grari, Sylvain Lamprier, Marcin Detyniecki
In recent years, fairness has become an important topic in the machine learning research community. In particular, counterfactual fairness aims at building prediction models which…
Fair Adversarial Gradient Tree Boosting
Vincent Grari, Boris Ruf, Sylvain Lamprier +1
Fair classification has become an important topic in machine learning research. While most bias mitigation strategies focus on neural networks, we noticed a lack of work on fair cl…
The Dangers of Post-hoc Interpretability: Unjustified Counterfactual Explanations
Thibault Laugel, Marie-Jeanne Lesot, Christophe Marsala +2
Post-hoc interpretability approaches have been proven to be powerful tools to generate explanations for the predictions made by a trained black-box model. However, they create the…
Issues with post-hoc counterfactual explanations: a discussion
Thibault Laugel, Marie-Jeanne Lesot, Christophe Marsala +1
Counterfactual post-hoc interpretability approaches have been proven to be useful tools to generate explanations for the predictions of a trained blackbox classifier. However, the…
Detecting Adversarial Examples and Other Misclassifications in Neural Networks by Introspection
Jonathan Aigrain, Marcin Detyniecki
Despite having excellent performances for a wide variety of tasks, modern neural networks are unable to provide a reliable confidence value allowing to detect misclassifications. T…