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08/29/2023
Probabilistic Dataset Reconstruction from Interpretable Models
Interpretability is often pointed out as a key requirement for trustwort...
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07/24/2023
Fairness Under Demographic Scarce Regime
Most existing works on fairness assume the model has full access to demo...
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03/08/2023
Learning Hybrid Interpretable Models: Theory, Taxonomy, and Methods
A hybrid model involves the cooperation of an interpretable model and a ...
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09/02/2022
Exploiting Fairness to Enhance Sensitive Attributes Reconstruction
In recent years, a growing body of work has emerged on how to learn mach...
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05/30/2022
Fooling SHAP with Stealthily Biased Sampling
SHAP explanations aim at identifying which features contribute the most ...
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05/15/2022
Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations
As post hoc explanation methods are increasingly being leveraged to expl...
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06/14/2021
Characterizing the risk of fairwashing
Fairwashing refers to the risk that an unfair black-box model can be exp...
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09/03/2020
Model extraction from counterfactual explanations
Post-hoc explanation techniques refer to a posteriori methods that can b...
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09/26/2019
GAMIN: An Adversarial Approach to Black-Box Model Inversion
Recent works have demonstrated that machine learning models are vulnerab...
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09/09/2019
Learning Fair Rule Lists
The widespread use of machine learning models, especially within the con...
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06/19/2019
Agnostic data debiasing through a local sanitizer learnt from an adversarial network approach
The widespread use of automated decision processes in many areas of our ...
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01/28/2019