26 citations · 66 across the 4 of their papers we have counts for
6 papers
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…
Privacy in trajectory micro-data publishing : a survey
Marco Fiore, Panagiota Katsikouli, Elli Zavou +7
We survey the literature on the privacy of trajectory micro-data, i.e., spatiotemporal information about the mobility of individuals, whose collection is becoming increasingly simp…
Fairwashing: the risk of rationalization
Ulrich Aïvodji, Hiromi Arai, Olivier Fortineau +3
Black-box explanation is the problem of explaining how a machine learning model -- whose internal logic is hidden to the auditor and generally complex -- produces its outcomes. Cur…