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Title: Learning to Explain Selectively: A Case Study on Question Answering
Explanations promise to bridge the gap between humans and AI, yet it remains difficult to achieve consistent improvement in AI-augmented human decision making. The usefulness of AI explanations depends on many factors, and always showing the same type of explanation in all cases is suboptimal—so is relying on heuristics to adapt explanations for each scenario. We propose learning to explain”selectively”: for each decision that the user makes, we use a model to choose the best explanation from a set of candidates and update this model with feedback to optimize human performance. We experiment on a question answering task, Quizbowl, and show that selective explanations improve human performance for both experts and crowdworkers.  more » « less
Award ID(s):
1822494
PAR ID:
10451415
Author(s) / Creator(s):
;
Date Published:
Journal Name:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Page Range / eLocation ID:
8372 to 8382
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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