270 citations · 669 across the 40 of their papers we have counts for
5 papers · 1 filter
AI Explainability 360: Impact and Design
Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen +17
As artificial intelligence and machine learning algorithms become increasingly prevalent in society, multiple stakeholders are calling for these algorithms to provide explanations.…
Let the CAT out of the bag: Contrastive Attributed explanations for Text
Saneem Chemmengath, Amar Prakash Azad, Ronny Luss +1
Contrastive explanations for understanding the behavior of black box models has gained a lot of attention recently as they provide potential for recourse. In this paper, we propose…
Multihop: Leveraging Complex Models to Learn Accurate Simple Models
Amit Dhurandhar, Tejaswini Pedapati
Knowledge transfer from a complex high performing model to a simpler and potentially low performing one in order to enhance its performance has been of great interest over the last…
When Stability meets Sufficiency: Informative Explanations that do not Overwhelm
Ronny Luss, Amit Dhurandhar
Recent studies evaluating various criteria for explainable artificial intelligence (XAI) suggest that fidelity, stability, and comprehensibility are among the most important metric…
Treatment Effect Estimation using Invariant Risk Minimization
Abhin Shah, Kartik Ahuja, Karthikeyan Shanmugam +3
Inferring causal individual treatment effect (ITE) from observational data is a challenging problem whose difficulty is exacerbated by the presence of treatment assignment bias. In…