92 citations · 211 across the 21 of their papers we have counts for
4 papers · 1 filter
On the Alignment of Group Fairness with Attribute Privacy
Jan Aalmoes, Vasisht Duddu, Antoine Boutet
Group fairness and privacy are fundamental aspects in designing trustworthy machine learning models. Previous research has highlighted conflicts between group fairness and differen…
Comprehension from Chaos: Towards Informed Consent for Private Computation
Bailey Kacsmar, Vasisht Duddu, Kyle Tilbury +2
Private computation, which includes techniques like multi-party computation and private query execution, holds great promise for enabling organizations to analyze data they and the…
Inferring Sensitive Attributes from Model Explanations
Vasisht Duddu, Antoine Boutet
Model explanations provide transparency into a trained machine learning model's blackbox behavior to a model builder. They indicate the influence of different input attributes to i…
Dikaios: Privacy Auditing of Algorithmic Fairness via Attribute Inference Attacks
Jan Aalmoes, Vasisht Duddu, Antoine Boutet
Machine learning (ML) models have been deployed for high-stakes applications. Due to class imbalance in the sensitive attribute observed in the datasets, ML models are unfair on mi…