Diversity and Inclusion Metrics in Subset Selection
Open accessAbstract
The ethical concept of fairness has recently been applied in machine learning (ML) settings to describe a wide range of constraints and objectives. When considering the relevance of ethical concepts to subset selection problems, the concepts of diversity and inclusion are additionally applicable in order to create outputs that account for social power and access differentials. We introduce metrics based on these concepts, which can be applied together, separately, and in tandem with additional fairness constraints. Results from human subject experiments lend support to the proposed criteria. Social choice methods can additionally be leveraged to aggregate and choose preferable sets, and we detail how these may be applied.
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Algorithmic bias can amplify and exacerbate existing social, cultural, or historical biases, leading to unfair treatment or discrimination against certain individuals or groups.
“It can amplify and exacerbate existing social, cultural, or historical biases, leading to unfair treatment or discrimination against certain individuals or groups [50-52].”
From Ethical Considerations of Using ChatGPT in Health Care (2023) · cites this paper as [52]
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Perez-Salazar, S., Torrico, A., & Verdugo, V. (2021) · ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO) · doi:10.1145/3465416.3483297 · Cited by 1 · see citing works
Furuya, K., Yücel, Z., Supitayakul, P., & Monden, A. (2022) · IIAI International Congress on Advanced Applied Informatics (IIAI-AAI) · doi:10.1109/iiaiaai55812.2022.00076 · Cited by 1 · see citing works
Ljubenkov, D. (2021) · CMI International Conference (CMI) · doi:10.1109/cmi53512.2021.9663855
Revisiting the Algorithm RBSC-SubGen:
Supitayakul, P., Furuya, K., Yücel, Z., Monden, A., & Leelaprute, P. (2023) · Information Engineering Express · doi:10.52731/iee.v9.i2.744
Fair&Share: Fast and Fair Multi-Criteria Selections
Cachel, K., & Rundensteiner, E. A. (2023) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3583780.3614874
Bartolo, L. (2024) · Queensland University of Technology · doi:10.5204/thesis.eprints.247841
RETRACTED: Algorithmic Bias: An Integrative Review and Scope for Future ResearchPreprint
Chaudhary, A. (2024) · Research Square · doi:10.21203/rs.3.rs-4775268/v1 · Cited by 1 · see citing works
RetractedUsing multiwinner voting to search for movies
Gawron, G., & Faliszewski, P. (2024) · Theory and Decision · doi:10.1007/s11238-024-10012-0
Towards Inclusive Cybersecurity: Protecting the Vulnerable with Social Cyber Vulnerability Metrics
Mitra, S., Zhang, Q., Chang, C.-W., Salemi, H., Purohit, H., Zhang, F., Hong, M., Lu, C., & Cho, J.-H. (2024) · IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA) · doi:10.1109/tps-isa62245.2024.00059
Tracing Bias for Fairer Content-Based Misinformation Detection
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Measuring Hidden Bias within Face Recognition via Racial Phenotypes [other]
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- Authors
- Mitchell, M., Baker, D., Moorosi, N., Denton, R., Hutchinson, B., Hanna, A., Gebru, T., & Morgenstern, J.
- Affiliations
- Google (United States) (US)
- Venue
- Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society
- Publisher
- ACM
- Published
- 2020-02-07
- Pages
- 117-123
- ISSN
- 3065-8365 (ISSN-L)
- OpenAlex
- W3005222337
- Cited by
- 87
- References
- 19
- Citations by year
- 2026: 2 · 2025: 11 · 2024: 13 · 2023: 16 · 2022: 27 · 2021: 13 · 2020: 5
- FWCI
- 15.82
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Proceedings of the AAAI/ACM Conference on AI Ethics and Society (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Game Theory and Voting Systems
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
- Keywords
- diversity, inclusion, subset selection, fairness metrics, social choice, social power, fairness constraints, human subjects research, algorithmic fairness
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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