Explanations, Fairness, and Appropriate Reliance in Human-AI Decision-Making-Reference-Cited by-同舟云学术

Explanations, Fairness, and Appropriate Reliance in Human-AI Decision-Making

Published:2024-05-11 Issue: Volume: Page:1-18
ISSN:
Container-title:Proceedings of the CHI Conference on Human Factors in Computing Systems
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Author:

Schoeffer Jakob¹^ORCID,De-Arteaga Maria¹^ORCID,Kühl Niklas²^ORCID

Affiliation:

1. The University of Texas at Austin, USA

2. University of Bayreuth, Germany

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3613904.3642621

Reference129 articles.

1. Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)

2. Ulrich Aïvodji, Hiromi Arai, Olivier Fortineau, Sébastien Gambs, Satoshi Hara, and Alain Tapp. 2019. Fairwashing: The risk of rationalization. In International Conference on Machine Learning. PMLR, 161–170.

3. The role of Artificial Intelligence in Online Dispute Resolution: A brief and critical overview

4. Does Explainable Artificial Intelligence Improve Human Decision-Making?

5. Guilherme Alves, Vaishnavi Bhargava, Miguel Couceiro, and Amedeo Napoli. 2021. Making ML models fairer through explanations: The case of LimeOut. In Analysis of Images, Social Networks and Texts: 9th International Conference, AIST 2020, Skolkovo, Moscow, Russia, October 15–16, 2020, Revised Selected Papers 9. Springer, 3–18.

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