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        <title>Learning to Explain Selectively: A Case Study on Question Answering</title>
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    <abstract>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.</abstract>
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    <identifier type="doi">10.18653/v1/2022.emnlp-main.573</identifier>
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%0 Conference Proceedings
%T Learning to Explain Selectively: A Case Study on Question Answering
%A Feng, Shi
%A Boyd-Graber, Jordan
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F feng-boyd-graber-2022-learning
%X 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.
%R 10.18653/v1/2022.emnlp-main.573
%U https://aclanthology.org/2022.emnlp-main.573
%U https://doi.org/10.18653/v1/2022.emnlp-main.573
%P 8372-8382

Markdown (Informal)

[Learning to Explain Selectively: A Case Study on Question Answering](https://aclanthology.org/2022.emnlp-main.573) (Feng & Boyd-Graber, EMNLP 2022)

• Learning to Explain Selectively: A Case Study on Question Answering (Feng & Boyd-Graber, EMNLP 2022)

ACL

• Shi Feng and Jordan Boyd-Graber. 2022. Learning to Explain Selectively: A Case Study on Question Answering. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 8372–8382, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.