mlsanityData to 7 Oct 2026

Diversity and Inclusion Metrics in Subset Selection

arXiv 2002.03256 ↗First posted 9 Feb 2020cs.AIcited by 27

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Abstract (arXiv)

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.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Feb 2020.

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Citing papers per year: 2021: 5; 2022: 12; 2023: 5; 2024: 5.

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  1. 2022The Vendi Score: A Diversity Evaluation Metric for Machine Learningcited by 299
  2. 2022DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generation Modelscited by 230
  3. 2021Gender Bias in Machine Translationcited by 116
  4. 2024Survey of Bias In Text-to-Image Generation: Definition, Evaluation, and Mitigationcited by 66
  5. 2022SERENGETI: Massively Multilingual Language Models for Africacited by 40
  6. 2024GenderBias-VL: Benchmarking Gender Bias in Vision Language Models via Counterfactual Probingcited by 31
  7. 2022Underspecification in Scene Description-to-Depiction Taskscited by 30
  8. 2023A Friendly Face: Do Text-to-Image Systems Rely on Stereotypes when the Input is Under-Specified?cited by 25
  9. 2023Consensus and Subjectivity of Skin Tone Annotation for ML Fairnesscited by 22
  10. 2022Listening to Affected Communities to Define Extreme Speech: Dataset and Experimentscited by 21
  11. 2022Bias in Automated Speaker Recognitioncited by 20
  12. 2022Measuring Datacited by 18
15 more
  1. 2021Measuring Hidden Bias within Face Recognition via Racial Phenotypescited by 16
  2. 2022Robots Enact Malignant Stereotypescited by 14
  3. 2022Justice in Misinformation Detection Systems: An Analysis of Algorithms, Stakeholders, and Potential Harmscited by 10
  4. 2023Racial Bias within Face Recognition: A Surveycited by 9
  5. 2022Tiny, always-on and fragile: Bias propagation through design choices in on-device machine learning workflowscited by 8
  6. 2022RADio -- Rank-Aware Divergence Metrics to Measure Normative Diversity in News Recommendationscited by 7
  7. 2021Social Norm Bias: Residual Harms of Fairness-Aware Algorithmscited by 6
  8. 2022Data Representativeness in Accessibility Datasets: A Meta-Analysiscited by 6
  9. 2021AI and Blackness: Towards moving beyond bias and representationcited by 4
  10. 2023Challenges and Solutions in AI for Allcited by 3
  11. 2021Deep Learning for Bias Detection: From Inception to Deploymentcited by 2
  12. 2023Designing Data: Proactive Data Collection and Iteration for Machine Learningcited by 2
  13. 2024Towards Equitable Agile Research and Development of AI and Roboticscited by 1
  14. 2024Diversity and Inclusion Index with Networks and Similarity: Analysis and its Applicationcited by 0
  15. 2024Text-to-Image Representativity Fairness Evaluation Frameworkcited by 0