758 citations · 1.8k across the 31 of their papers we have counts for
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Assessing the Fairness of AI Systems: AI Practitioners' Processes, Challenges, and Needs for Support
Michael Madaio, Lisa Egede, Hariharan Subramonyam +2
Various tools and practices have been developed to support practitioners in identifying, assessing, and mitigating fairness-related harms caused by AI systems. However, prior resea…
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…
Manipulating and Measuring Model Interpretability
Forough Poursabzi-Sangdeh, Daniel G. Goldstein, Jake M. Hofman +2
With machine learning models being increasingly used to aid decision making even in high-stakes domains, there has been a growing interest in developing interpretable models. Altho…
Inferring Multilateral Relations from Dynamic Pairwise Interactions
Aaron Schein, Juston Moore, Hanna Wallach
Correlations between anomalous activity patterns can yield pertinent information about complex social processes: a significant deviation from normal behavior, exhibited simultaneou…