69 citations · 69 across the 1 of their papers we have counts for
2 papers
cs.HC2022★ 69 cited
Sensible AI: Re-imagining Interpretability and Explainability using Sensemaking Theory
Harmanpreet Kaur, Eytan Adar, Eric Gilbert +1
Understanding how ML models work is a prerequisite for responsibly designing, deploying, and using ML-based systems. With interpretability approaches, ML can now offer explanations…
cs.AI2021
From Human Explanation to Model Interpretability: A Framework Based on Weight of Evidence
David Alvarez-Melis, Harmanpreet Kaur, Hal Daumé +2
We take inspiration from the study of human explanation to inform the design and evaluation of interpretability methods in machine learning. First, we survey the literature on huma…