Ground(less) Truth: A Causal Framework for Proxy Labels in Human-Algorithm Decision-Making-Reference-Cited by-同舟云学术

Ground(less) Truth: A Causal Framework for Proxy Labels in Human-Algorithm Decision-Making

Published:2023-06-12 Issue: Volume: Page:
ISSN:
Container-title:2023 ACM Conference on Fairness, Accountability, and Transparency
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Author:

Guerdan Luke¹^ORCID,Coston Amanda¹^ORCID,Wu Zhiwei Steven¹^ORCID,Holstein Kenneth¹^ORCID

Affiliation:

1. Carnegie Mellon University, USA

Funder

UL Research Institutes through the Center for Advancing Safety of Machine Intelligence (CASMI) at Northwestern University

National Science Foundation Graduate Research Fellowship Program

Carnegie Mellon University Block Center for Technology and Society

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3593013.3594036

Reference102 articles.

1. The effect of differential victim crime reporting on predictive policing systems

2. Keaven M Anderson , Patricia M Odell , Peter WF Wilson, and William B Kannel . 1991 . Cardiovascular disease risk profiles. American heart journal 121, 1 (1991), 293–298. Keaven M Anderson, Patricia M Odell, Peter WF Wilson, and William B Kannel. 1991. Cardiovascular disease risk profiles. American heart journal 121, 1 (1991), 293–298.

3. Julia Angwin , Jeff Larson , Surya Mattu , and Lauren Kirchner . 2016. Machine bias . In Ethics of Data and Analytics . Auerbach Publications , 254–264. Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner. 2016. Machine bias. In Ethics of Data and Analytics. Auerbach Publications, 254–264.

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

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. arXiv preprint arXiv:2106.05498 ( 2021 ). 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. arXiv preprint arXiv:2106.05498 (2021).

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