Towards Balancing Preference and Performance through Adaptive Personalized Explainability-Reference-Cited by-同舟云学术

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Towards Balancing Preference and Performance through Adaptive Personalized Explainability

Published:2024-03-11 Issue: Volume:2099 Page:658-668
ISSN:
Container-title:Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction
language:
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Author:

Silva Andrew¹^ORCID,Tambwekar Pradyumna²^ORCID,Schrum Mariah³^ORCID,Gombolay Matthew²^ORCID

Affiliation:

1. Georgia Institute of Technology & Toyota Research Institute, Atlanta, GA, USA

2. Georgia Institute of Technology, Atlanta, GA, USA

3. School of Interactive Computing, Georgia Institute of Technology & Robotics, University of California, Berkeley, Atlanta, GA, USA

Funder

NASA

NSF

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3610977.3635000

Reference113 articles.

1. A Theoretical and Empirical Reflection on Technology Acceptance Models for Autonomous Delivery Robots

2. Mental Models of Mere Mortals with Explanations of Reinforcement Learning

3. Manoj Ghuhan Arivazhagan, Vinay Aggarwal, Aaditya Kumar Singh, and Sunav Choudhary. 2019. Federated learning with personalization layers. arXiv preprint arXiv:1912.00818 (2019).

4. Vijay Arya, Rachel KE Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C Hoffman, Stephanie Houde, Q Vera Liao, Ronny Luss, Aleksandra Mojsilovi?, et al. 2019. One explanation does not fit all: A toolkit and taxonomy of ai explainability techniques. arXiv preprint arXiv:1909.03012 (2019).

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