Subgoal-Based Explanations for Unreliable Intelligent Decision Support Systems

Author:

Das Devleena1ORCID,Kim Been2ORCID,Chernova Sonia3ORCID

Affiliation:

1. Georgia Institute of Technology, United States

2. Google, United States

3. Interactive Computing, Georgia Institute of Technology, United States

Publisher

ACM

Reference47 articles.

1. Amina Adadi and Mohammed Berrada . 2018. Peeking inside the black-box: a survey on explainable artificial intelligence (XAI) . IEEE access 6( 2018 ), 52138–52160. Amina Adadi and Mohammed Berrada. 2018. Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE access 6(2018), 52138–52160.

2. Julius Adebayo Michael Muelly Ilaria Liccardi and Been Kim. 2020. Debugging tests for model explanations. arXiv preprint arXiv:2011.05429(2020). Julius Adebayo Michael Muelly Ilaria Liccardi and Been Kim. 2020. Debugging tests for model explanations. arXiv preprint arXiv:2011.05429(2020).

3. Impact of intelligent decision aids on expert and novice decision-makers' judgments

4. Gerard Canal , Senka Krivic , Paul Luff , and Andrew Coles . 2021 . Task Plan verbalizations with causal justifications . In ICAPS 2021 Workshop on Explainable AI Planning (XAIP). Gerard Canal, Senka Krivic, Paul Luff, and Andrew Coles. 2021. Task Plan verbalizations with causal justifications. In ICAPS 2021 Workshop on Explainable AI Planning (XAIP).

5. Micah Carroll , Rohin Shah , Mark  K Ho , Tom Griffiths , Sanjit Seshia , Pieter Abbeel , and Anca Dragan . 2019. On the utility of learning about humans for human-ai coordination. NeurIPS ( 2019 ). Micah Carroll, Rohin Shah, Mark K Ho, Tom Griffiths, Sanjit Seshia, Pieter Abbeel, and Anca Dragan. 2019. On the utility of learning about humans for human-ai coordination. NeurIPS (2019).

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