36 citations · 46 across the 21 of their papers we have counts for
4 papers · 1 filter
Efficient Bias Mitigation Without Privileged Information
Mateo Espinosa Zarlenga, Swami Sankaranarayanan, Jerone T. A. Andrews +3
Deep neural networks trained via empirical risk minimisation often exhibit significant performance disparities across groups, particularly when group and task labels are spuriously…
Understanding Inter-Concept Relationships in Concept-Based Models
Naveen Raman, Mateo Espinosa Zarlenga, Mateja Jamnik
Concept-based explainability methods provide insight into deep learning systems by constructing explanations using human-understandable concepts. While the literature on human reas…
Causal Concept Graph Models: Beyond Causal Opacity in Deep Learning
Gabriele Dominici, Pietro Barbiero, Mateo Espinosa Zarlenga +4
Causal opacity denotes the difficulty in understanding the "hidden" causal structure underlying the decisions of deep neural network (DNN) models. This leads to the inability to re…
Do Concept Bottleneck Models Respect Localities?
Naveen Raman, Mateo Espinosa Zarlenga, Juyeon Heo +1
Concept-based explainability methods use human-understandable intermediaries to produce explanations for machine learning models. These methods assume concept predictions can help…