Is this AI trained on Credible Data? The Effects of Labeling Quality and Performance Bias on User Trust-Reference-Cited by-同舟云学术

Is this AI trained on Credible Data? The Effects of Labeling Quality and Performance Bias on User Trust

Published:2023-04-19 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
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Author:

Chen Cheng¹^ORCID,Sundar S. Shyam²^ORCID

Affiliation:

1. School of Communications, Elon University, United States

2. Media Effects Research Laboratory, The Pennsylvania State University, United States

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3544548.3580805

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4. Alejandro Barredo Arrieta Natalia Díaz-Rodríguez Javier Del Ser Adrien Bennetot Siham Tabik Alberto Barbado Salvador García Sergio Gil-López Daniel Molina Richard Benjamins 2020. Explainable Artificial Intelligence (XAI): Concepts taxonomies opportunities and challenges toward responsible AI. Information fusion 58(2020) 82-115. https://doi.org/10.1016/j.inffus.2019.12.012

5. Michelle Bao , Angela Zhou , Samantha Zottola , Brian Brubach , Sarah Desmarais , Aaron Horowitz , Kristian Lum , and Suresh Venkatasubramanian . 2021 . It’s COMPASlicated: The Messy Relationship between RAI Datasets and Algorithmic Fairness Benchmarks . In Proceedings of the 35th Conference on Neutral Information Proceesing Systems. https://doi.org/10 .48550/arXiv.2106.05498 10.48550/arXiv.2106.05498 Michelle Bao, Angela Zhou, Samantha Zottola, Brian Brubach, Sarah Desmarais, Aaron Horowitz, Kristian Lum, and Suresh Venkatasubramanian. 2021. It’s COMPASlicated: The Messy Relationship between RAI Datasets and Algorithmic Fairness Benchmarks. In Proceedings of the 35th Conference on Neutral Information Proceesing Systems. https://doi.org/10.48550/arXiv.2106.05498

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