90 citations · 106 across the 5 of their papers we have counts for
4 papers · 1 filter
On the Importance of Application-Grounded Experimental Design for Evaluating Explainable ML Methods
Kasun Amarasinghe, Kit T. Rodolfa, Sérgio Jesus +6
Most existing evaluations of explainable machine learning (ML) methods rely on simplifying assumptions or proxies that do not reflect real-world use cases; the handful of more robu…
ConceptDistil: Model-Agnostic Distillation of Concept Explanations
João Bento Sousa, Ricardo Moreira, Vladimir Balayan +2
Concept-based explanations aims to fill the model interpretability gap for non-technical humans-in-the-loop. Previous work has focused on providing concepts for specific models (eg…
Weakly Supervised Multi-task Learning for Concept-based Explainability
Catarina Belém, Vladimir Balayan, Pedro Saleiro +1
In ML-aided decision-making tasks, such as fraud detection or medical diagnosis, the human-in-the-loop, usually a domain-expert without technical ML knowledge, prefers high-level c…
Teaching the Machine to Explain Itself using Domain Knowledge
Vladimir Balayan, Pedro Saleiro, Catarina Belém +2
Machine Learning (ML) has been increasingly used to aid humans to make better and faster decisions. However, non-technical humans-in-the-loop struggle to comprehend the rationale b…