47 citations · 214 across the 25 of their papers we have counts for
4 papers · 2 filters
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…
Adversarial training approach for local data debiasing
Ulrich Aïvodji, François Bidet, Sébastien Gambs +2
The widespread use of automated decision processes in many areas of our society raises serious ethical issues concerning the fairness of the process and the possible resulting disc…
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…