26 citations · 66 across the 4 of their papers we have counts for
7 papers · 1 filter
Fairness Under Demographic Scarce Regime
Patrik Joslin Kenfack, Samira Ebrahimi Kahou, Ulrich Aïvodji
Most existing works on fairness assume the model has full access to demographic information. However, there exist scenarios where demographic information is partially available bec…
Learning Hybrid Interpretable Models: Theory, Taxonomy, and Methods
Julien Ferry, Gabriel Laberge, Ulrich Aïvodji
A hybrid model involves the cooperation of an interpretable model and a complex black box. At inference, any input of the hybrid model is assigned to either its interpretable or co…
Characterizing the risk of fairwashing
Ulrich Aïvodji, Hiromi Arai, Sébastien Gambs +1
Fairwashing refers to the risk that an unfair black-box model can be explained by a fairer model through post-hoc explanation manipulation. In this paper, we investigate the capabi…
Model extraction from counterfactual explanations
Ulrich Aïvodji, Alexandre Bolot, Sébastien Gambs
Post-hoc explanation techniques refer to a posteriori methods that can be used to explain how black-box machine learning models produce their outcomes. Among post-hoc explanation t…
GAMIN: An Adversarial Approach to Black-Box Model Inversion
Ulrich Aïvodji, Sébastien Gambs, Timon Ther
Recent works have demonstrated that machine learning models are vulnerable to model inversion attacks, which lead to the exposure of sensitive information contained in their traini…
Learning Fair Rule Lists
Ulrich Aïvodji, Julien Ferry, Sébastien Gambs +2
As the use of black-box models becomes ubiquitous in high stake decision-making systems, demands for fair and interpretable models are increasing. While it has been shown that inte…