8 citations · 16 across the 4 of their papers we have counts for
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cs.LG2020
Consumer-Driven Explanations for Machine Learning Decisions: An Empirical Study of Robustness
Michael Hind, Dennis Wei, Yunfeng Zhang
Many proposed methods for explaining machine learning predictions are in fact challenging to understand for nontechnical consumers. This paper builds upon an alternative consumer-d…
cs.LG2019★ 2 cited
Teaching AI to Explain its Decisions Using Embeddings and Multi-Task Learning
Noel C. F. Codella, Michael Hind, Karthikeyan Natesan Ramamurthy +5
Using machine learning in high-stakes applications often requires predictions to be accompanied by explanations comprehensible to the domain user, who has ultimate responsibility f…