44 citations · 104 across the 47 of their papers we have counts for
3 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…
Use-Case-Grounded Simulations for Explanation Evaluation
Valerie Chen, Nari Johnson, Nicholay Topin +2
A growing body of research runs human subject evaluations to study whether providing users with explanations of machine learning models can help them with practical real-world use…
Perspectives on Incorporating Expert Feedback into Model Updates
Valerie Chen, Umang Bhatt, Hoda Heidari +2
Machine learning (ML) practitioners are increasingly tasked with developing models that are aligned with non-technical experts' values and goals. However, there has been insufficie…