Search Results for author:
Found 12 papers, 7 papers with code
SoK: Taming the Triangle -- On the Interplays between Fairness, Interpretability and Privacy in Machine Learning
Machine learning techniques are increasingly used for high-stakes decision-making, such as college admissions, loan attribution or recidivism prediction.
Probabilistic Dataset Reconstruction from Interpretable Models
In addition, we demonstrate that under realistic assumptions regarding the interpretable models' structure, the uncertainty of the reconstruction can be computed efficiently.
Fairness Under Demographic Scarce Regime
Surprisingly, our framework outperforms models trained with constraints on the true sensitive attributes.
Learning Hybrid Interpretable Models: Theory, Taxonomy, and Methods
The advantages of such models over classical ones are two-fold: 1) They grant users precise control over the level of transparency of the system and 2) They can potentially perform better than a standalone black box since redirecting some of the inputs to an interpretable model implicitly acts as regularization.
Exploiting Fairness to Enhance Sensitive Attributes Reconstruction
More precisely, we propose a generic reconstruction correction method, which takes as input an initial guess made by the adversary and corrects it to comply with some user-defined constraints (such as the fairness information) while minimizing the changes in the adversary's guess.
Fool SHAP with Stealthily Biased Sampling
SHAP explanations aim at identifying which features contribute the most to the difference in model prediction at a specific input versus a background distribution.
Characterizing the risk of fairwashing
In particular, we show that fairwashed explanation models can generalize beyond the suing group (i. e., data points that are being explained), meaning that a fairwashed explainer can be used to rationalize subsequent unfair decisions of a black-box model.
Model extraction from counterfactual explanations
Post-hoc explanation techniques refer to a posteriori methods that can be used to explain how black-box machine learning models produce their outcomes.
GAMIN: An Adversarial Approach to Black-Box Model Inversion
While some model inversion attacks have been developed in the past in the black-box attack setting, in which the adversary does not have direct access to the structure of the model, few of these have been conducted so far against complex models such as deep neural networks.
Learning Fair Rule Lists
While it has been shown that interpretable models can be as accurate as black-box models in several critical domains, existing fair classification techniques that are interpretable by design often display poor accuracy/fairness tradeoffs in comparison with their non-interpretable counterparts.
Adversarial training approach for local data debiasing
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 discriminations.
Fairwashing: the risk of rationalization
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.
