mlsanityData to 7 Oct 2026

Whose Ground Truth? Accounting for Individual and Collective Identities Underlying Dataset Annotation

arXiv 2112.04554 ↗First posted 8 Dec 2021cs.LGcited by 51

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Abstract (arXiv)

Human annotations play a crucial role in machine learning (ML) research and development. However, the ethical considerations around the processes and decisions that go into building ML datasets has not received nearly enough attention. In this paper, we survey an array of literature that provides insights into ethical considerations around crowdsourced dataset annotation. We synthesize these insights, and lay out the challenges in this space along two layers: (1) who the annotator is, and how the annotators' lived experiences can impact their annotations, and (2) the relationship between the annotators and the crowdsourcing platforms and what that relationship affords them. Finally, we put forth a concrete set of recommendations and considerations for dataset developers at various stages of the ML data pipeline: task formulation, selection of annotators, platform and infrastructure choices, dataset analysis and evaluation, and dataset documentation and release.

Key facts. Cited by 51 papers in the map (data through 7 Oct 2026). Builds on 9 of its 30 references that are in the map. First posted on arXiv 8 Dec 2021.

Builds on

9 of 30 references.

References · 9

  1. 2018Datasheets for Datasetscited by 1,371
  2. 2020Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructurecited by 138
  3. 2021On Releasing Annotator-Level Labels and Information in Datasetscited by 123
  4. 2021Re-imagining Algorithmic Fairness in India and Beyondcited by 102
  5. 2018The Dataset Nutrition Label: A Framework To Drive Higher Data Quality Standardscited by 97
  6. 2021Do Datasets Have Politics? Disciplinary Values in Computer Vision Dataset Developmentcited by 85
  7. 2019Garbage In, Garbage Out? Do Machine Learning Application Papers in Social Computing Report Where Human-Labeled Training Data Comes From?cited by 64
  8. 2021Beyond Fair Pay: Ethical Implications of NLP Crowdsourcingcited by 52
  9. 2018Characterizing the Global Crowd Workforce: A Cross-Country Comparison of Crowdworker Demographicscited by 10

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 7; 2023: 18; 2024: 12; 2025: 6; 2026: 8.

51 papers.

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  1. 2022The 'Problem' of Human Label Variation: On Ground Truth in Data, Modeling and Evaluationcited by 133
  2. 2024On the Algorithmic Bias of Aligning Large Language Models with RLHF: Preference Collapse and Matching Regularizationcited by 72
  3. 2023FACET: Fairness in Computer Vision Evaluation Benchmarkcited by 58
  4. 2023AI's Regimes of Representation: A Community-centered Study of Text-to-Image Models in South Asiacited by 54
  5. 2023Supporting Human-AI Collaboration in Auditing LLMs with LLMscited by 46
  6. 2023"HOT" ChatGPT: The promise of ChatGPT in detecting and discriminating hateful, offensive, and toxic comments on social mediacited by 45
  7. 2024Content Moderation by LLM: From Accuracy to Legitimacycited by 30
  8. 2023Capturing Perspectives of Crowdsourced Annotators in Subjective Learning Taskscited by 28
  9. 2024Farsight: Fostering Responsible AI Awareness During AI Application Prototypingcited by 28
  10. 2023Ground(less) Truth: A Causal Framework for Proxy Labels in Human-Algorithm Decision-Makingcited by 25
  11. 2023It Takes Two to Tango: Navigating Conceptualizations of NLP Tasks and Measurements of Performancecited by 25
  12. 2022Labeling instructions matter in biomedical image analysiscited by 20
38 more
  1. 2025Direct Alignment with Heterogeneous Preferencescited by 19
  2. 2024Wikibench: Community-Driven Data Curation for AI Evaluation on Wikipediacited by 18
  3. 2022Subgroup Robustness Grows On Trees: An Empirical Baseline Investigationcited by 17
  4. 2023Ethical Considerations for Responsible Data Curationcited by 17
  5. 2023GRASP: A Disagreement Analysis Framework to Assess Group Associations in Perspectivescited by 16
  6. 2024Harm Amplification in Text-to-Image Modelscited by 14
  7. 2023Scaling Laws Do Not Scalecited by 13
  8. 2023Skin Deep: Investigating Subjectivity in Skin Tone Annotations for Computer Vision Benchmark Datasetscited by 11
  9. 2023Ground Truth Or Dare: Factors Affecting The Creation Of Medical Datasets For Training AIcited by 11
  10. 2024D3CODE: Disentangling Disagreements in Data across Cultures on Offensiveness Detection and Evaluationcited by 9
  11. 2024Discipline and Label: A WEIRD Genealogy and Social Theory of Data Annotationcited by 8
  12. 2025From LLM-anation to LLM-orchestrator: Coordinating Small Models for Data Labelingcited by 8
  13. 2022TRUE: Re-evaluating Factual Consistency Evaluationcited by 7
  14. 2022Noise Audits Improve Moral Foundation Classificationcited by 7
  15. 2024Don't Label Twice: Quantity Beats Quality when Comparing Binary Classifiers on a Budgetcited by 7
  16. 2023A View From Somewhere: Human-Centric Face Representationscited by 6
  17. 2024Whose Preferences? Differences in Fairness Preferences and Their Impact on the Fairness of AI Utilizing Human Feedbackcited by 6
  18. 2022The Risks of Machine Learning Systemscited by 5
  19. 2023Flickr Africa: Examining Geo-Diversity in Large-Scale, Human-Centric Visual Datacited by 5
  20. 2022Cultural Re-contextualization of Fairness Research in Language Technologies in Indiacited by 4
  21. 2023A Taxonomy of Rater Disagreements: Surveying Challenges & Opportunities from the Perspective of Annotating Online Toxicitycited by 4
  22. 2024Measuring Machine Learning Harms from Stereotypes Requires Understanding Who Is Harmed by Which Errors in What Wayscited by 4
  23. 2024Can Vision-Language Models Replace Human Annotators: A Case Study with CelebA Datasetcited by 4
  24. 2024Insights on Disagreement Patterns in Multimodal Safety Perception across Diverse Rater Groupscited by 4
  25. 2025Who Evaluates AI's Social Impacts? Mapping Coverage and Gaps in First and Third Party Evaluationscited by 4
  26. 2023Unveiling the Multi-Annotation Process: Examining the Influence of Annotation Quantity and Instance Difficulty on Model Performancecited by 3
  27. 2025Characterizing Network Structure of Anti-Trans Actors on TikTokcited by 1
  28. 2026The Consensus Trap: Dissecting Subjectivity and the "Ground Truth" Illusion in Data Annotationcited by 1
  29. 2026Differentially Private Preference Data Synthesis for Large Language Model Alignmentcited by 1
  30. 2023Eliciting Model Steering Interactions from Users via Data and Visual Design Probescited by 0
  31. 2023Socially Cognizant Robotics for a Technology Enhanced Societycited by 0
  32. 2025Advancing Data Equity: Practitioner Responsibility and Accountability in NLP Data Practicescited by 0
  33. 2025On the Sensitivity of Instruction-tuned LLMs to Harmful Sentences in Long Inputscited by 0
  34. 2026Learning Who Disagrees: Demographic Importance Weighting for Modeling Annotator Distributions with DiADEMcited by 0
  35. 2026IYKYK (But AI Doesn't): Automated Content Moderation Does Not Capture Communities' Heterogeneous Attitudes Towards Reclaimed Languagecited by 0
  36. 2026Beyond Majority Voting: Agreement-Based Clustering to Model Annotator Perspectives in Subjective NLP Taskscited by 0
  37. 2026Algorithmic Fragility and Persona Bias in LLM-Generated Autistic Communicationcited by 0
  38. 2026The Market in the Model: Latent Diffusion as Neural Economycited by 0

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