Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance-Reference-Cited by-同舟云学术

Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance

Published:2021-05-06 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
language:
Short-container-title:

Author:

Bansal Gagan¹,Wu Tongshuang¹,Zhou Joyce²,Fok Raymond²,Nushi Besmira³,Kamar Ece³,Ribeiro Marco Tulio³,Weld Daniel²

Affiliation:

1. University of Washington, United States

2. Paul G. Allen School of Computer Science & Engineering University of Washington, United States

3. Microsoft Research, United States

Funder

Microsoft Research

the University of Washington WRF/Cable Professorship

Office of Naval Research

NSF RAPID grant

the Allen Institute for Artificial Intelligence (AI2)

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3411764.3445717

Reference83 articles.

1. Julia Angwin Jeff Larson Surya Mattu and Lauren Kirchner. 2016. Machine bias: There’s software across the country to predict future criminals and it’s biased against blacks. Julia Angwin Jeff Larson Surya Mattu and Lauren Kirchner. 2016. Machine bias: There’s software across the country to predict future criminals and it’s biased against blacks.

2. Teaching Categories to Human Learners with Visual Explanations

3. Gagan Bansal Besmira Nushi Ece Kamar Eric Horvitz and Daniel S. Weld. 2020. Optimizing AI for Teamwork. arxiv:2004.13102 [cs.AI] Gagan Bansal Besmira Nushi Ece Kamar Eric Horvitz and Daniel S. Weld. 2020. Optimizing AI for Teamwork. arxiv:2004.13102 [cs.AI]

Cited by 226 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Majority voting of doctors improves appropriateness of AI reliance in pathology;International Journal of Human-Computer Studies;2024-10

2. To Err Is AI! Debugging as an Intervention to Facilitate Appropriate Reliance on AI Systems;Proceedings of the 35th ACM Conference on Hypertext and Social Media;2024-09-10

3. Evaluating quality of ontology-driven conceptual models abstractions;Data & Knowledge Engineering;2024-09

4. You Can Only Verify When You Know the Answer: Feature-Based Explanations Reduce Overreliance on AI for Easy Decisions, but Not for Hard Ones;Proceedings of Mensch und Computer 2024;2024-09

5. Human-AI Teamwork Interface Design Using Patterns of Interactions;International Journal of Human–Computer Interaction;2024-08-26