A Critical Survey on Fairness Benefits of Explainable AI-Reference-Cited by-同舟云学术

A Critical Survey on Fairness Benefits of Explainable AI

Published:2024-06-03 Issue: Volume: Page:
ISSN:
Container-title:The 2024 ACM Conference on Fairness, Accountability, and Transparency
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Author:

Deck Luca¹^ORCID,Schoeffer Jakob²^ORCID,De-Arteaga Maria³^ORCID,Kühl Niklas¹^ORCID

Affiliation:

1. University of Bayreuth, Germany

2. University of Texas at Austin, United States

3. University of Texas at Austin, USA

Funder

National Institutes of Health

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3630106.3658990

Reference237 articles.

1. Behnoush Abdollahi and Olfa Nasraoui. 2018. Transparency in fair machine learning: The case of explainable recommender systems. Human and Machine Learning: Visible, Explainable, Trustworthy and Transparent (2018), 21–35.

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

3. Black box fairness testing of machine learning models

4. Allocating Interventions Based on Predicted Outcomes: A Case Study on Homelessness Services

5. FairSight: Visual analytics for fairness in decision making;Ahn Yongsu;IEEE Transactions on Visualization and Computer Graphics,2020

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