Optimizing Decision-Maker's Intrinsic Motivation for Effective Human-AI Decision-Making

Author:

Buçinca Zana1ORCID

Affiliation:

1. School of Engineering and Applied Sciences, Harvard University, United States

Funder

NSF (National Science Foundation)

Publisher

ACM

Reference24 articles.

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

2. Proxy tasks and subjective measures can be misleading in evaluating explainable AI systems

3. Zana Buçinca, Alexandra Chouldechova, Jennifer Wortman Vaughan, and Krzysztof Z. Gajos. 2022. Beyond end predictions: stop putting machine learning first and design human-centered AI for decision support. NeurIPS Workshop on Human-Centered AI (HCAI) (2022).

4. To Trust or to Think

5. Zana Buçinca, Chau Minh Pham, Maurice Jakesch, Marco Tulio Ribeiro, Alexandra Olteanu, and Saleema Amershi. 2023. AHA!: Facilitating AI Impact Assessment by Generating Examples of Harms. arXiv e-prints (2023), arXiv–2306.

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