841 citations · 1.8k across the 15 of their papers we have counts for
3 papers · 1 filter
Evaluating the Fairness of Discriminative Foundation Models in Computer Vision
Junaid Ali, Matthaeus Kleindessner, Florian Wenzel +3
We propose a novel taxonomy for bias evaluation of discriminative foundation models, such as Contrastive Language-Pretraining (CLIP), that are used for labeling tasks. We then syst…
The Unfairness of Fair Machine Learning: Levelling down and strict egalitarianism by default
Brent Mittelstadt, Sandra Wachter, Chris Russell
In recent years fairness in machine learning (ML) has emerged as a highly active area of research and development. Most define fairness in simple terms, where fairness means reduci…
Causal Triplet: An Open Challenge for Intervention-centric Causal Representation Learning
Yuejiang Liu, Alexandre Alahi, Chris Russell +4
Recent years have seen a surge of interest in learning high-level causal representations from low-level image pairs under interventions. Yet, existing efforts are largely limited t…