7 citations · 18 across the 10 of their papers we have counts for
4 papers · 1 filter
SoK: Taming the Triangle -- On the Interplays between Fairness, Interpretability and Privacy in Machine Learning
Julien Ferry, Ulrich Aïvodji, Sébastien Gambs +2
Machine learning techniques are increasingly used for high-stakes decision-making, such as college admissions, loan attribution or recidivism prediction. Thus, it is crucial to ens…
Probabilistic Dataset Reconstruction from Interpretable Models
Julien Ferry, Ulrich Aïvodji, Sébastien Gambs +2
Interpretability is often pointed out as a key requirement for trustworthy machine learning. However, learning and releasing models that are inherently interpretable leaks informat…
Learning Optimal Fair Scoring Systems for Multi-Class Classification
Julien Rouzot, Julien Ferry, Marie-José Huguet
Machine Learning models are increasingly used for decision making, in particular in high-stakes applications such as credit scoring, medicine or recidivism prediction. However, the…
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…