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Cited by 24Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)Face recognition and analysis

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Fairness Indicators for Systematic Assessments of Visual Feature Extractors

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Goyal, Priya, Soriano, Adriana Romero, Hazirbas, Caner, Sagun, Levent, Usunier, Nicolas·2022·2022 ACM Conference on Fairness Accountability and Transparency
DOI 10.1145/3531146.3533074
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Abstract

Does everyone equally benefit from computer vision systems? Answers to this question become more and more important as computer vision systems are deployed at large scale, and can spark major concerns when they exhibit vast performance discrepancies between people from various demographic and social backgrounds.

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Crossref · 10.1145/3531146.3533074

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  • Human judgment might be required to determine which features are supposed to be relevant to the task.

    “However, human judgment might be required to determine which features are 'supposed' to be relevant to the task (Marks et al., 2024;Kim et al., 2018a;Goyal et al., 2022).”

    From Open Problems in Mechanistic Interpretability (2025) · cites this paper as Goyal et al., 2022

    In the label-association indicator section (page unavailable), the paper says that assessing whether certain predictions are correct requires manual visual inspection. That is different from deciding which features are supposed to be relevant to a task. Searches for task-relevant features and human selection found no passage addressing that decision. (LLM, conf=0.72)

    Experimental — accuracy not yet measured

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Metadata

Authors
Goyal, P., Soriano, A. R., Hazirbas, C., Sagun, L., & Usunier, N.
Affiliations
  • Meta (United States) (US)
  • Meta, Canada
  • Meta, USA
  • Meta, France
ORCID
Venue
2022 ACM Conference on Fairness Accountability and Transparency
Publisher
ACM
Published
2022-06-20
Pages
70-88
DOI
10.1145/3531146.3533074
OpenAlex
W4283170167
Cited by
24
References
6
Citations by year
2026: 2 · 2025: 3 · 2024: 6 · 2023: 9 · 2022: 4
FWCI
2.35
Type
conference paper
Language
en
Open access
Licence
Locations
  • ACM Conference on Fairness, Accountability, and Transparency (FAccT) (publishedVersion)
  • arXiv (Cornell University) (submittedVersion, cc-by)
  • arXiv (Cornell University) (cc-by)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
  • Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
Keywords
computer vision systems, algorithmic fairness, demographic bias, performance gap

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