Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Tools-Reference-Cited by-同舟云学术

Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Tools

Published:2023-10-30 Issue: Volume: Page:
ISSN:
Container-title:Equity and Access in Algorithms, Mechanisms, and Optimization
language:
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Author:

Black Emily¹^ORCID,Naidu Rakshit²^ORCID,Ghani Rayid³^ORCID,Rodolfa Kit⁴^ORCID,Ho Daniel⁴^ORCID,Heidari Hoda⁵^ORCID

Affiliation:

1. Barnard College, USA

2. Georgia Institute of Technology, USA

3. Carnegie Mellon University, USA

4. Stanford University, USA

5. Carnegie Mellon University, United States

Funder

National Science Foundation

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3617694.3623259

Reference112 articles.

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2. NIST AI. 2023. Artificial Intelligence Risk Management Framework (AI RMF 1.0). NIST AI. 2023. Artificial Intelligence Risk Management Framework (AI RMF 1.0).

3. Nil-Jana Akpinar Manish Nagireddy Logan Stapleton Hao-Fei Cheng Haiyi Zhu Steven Wu and Hoda Heidari. 2022. A Sandbox Tool to Bias (Stress)-Test Fairness Algorithms. Nil-Jana Akpinar Manish Nagireddy Logan Stapleton Hao-Fei Cheng Haiyi Zhu Steven Wu and Hoda Heidari. 2022. A Sandbox Tool to Bias (Stress)-Test Fairness Algorithms.

4. Aws Albarghouthi and Samuel Vinitsky. 2019. Fairness-aware programming. 211–219 pages. Aws Albarghouthi and Samuel Vinitsky. 2019. Fairness-aware programming. 211–219 pages.

5. Eugene Bagdasaryan Omid Poursaeed and Vitaly Shmatikov. 2019. Differential privacy has disparate impact on model accuracy. Eugene Bagdasaryan Omid Poursaeed and Vitaly Shmatikov. 2019. Differential privacy has disparate impact on model accuracy.

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