mlsanityData to 7 Oct 2026

Jury Learning: Integrating Dissenting Voices into Machine Learning Models

arXiv 2202.02950 ↗First posted 7 Feb 2022cs.HC cs.AI cs.LGcited by 115

Not on the map. Why?

Cited by 25 in LLM alignment / RLHF / preference optimisation, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Whose labels should a machine learning (ML) algorithm learn to emulate? For ML tasks ranging from online comment toxicity to misinformation detection to medical diagnosis, different groups in society may have irreconcilable disagreements about ground truth labels. Supervised ML today resolves these label disagreements implicitly using majority vote, which overrides minority groups' labels. We introduce jury learning, a supervised ML approach that resolves these disagreements explicitly through the metaphor of a jury: defining which people or groups, in what proportion, determine the classifier's prediction. For example, a jury learning model for online toxicity might centrally feature women and Black jurors, who are commonly targets of online harassment. To enable jury learning, we contribute a deep learning architecture that models every annotator in a dataset, samples from annotators' models to populate the jury, then runs inference to classify. Our architecture enables juries that dynamically adapt their composition, explore counterfactuals, and visualize dissent.

Key facts. Cited by 115 papers in the map (data through 7 Oct 2026). Builds on 30 of its 97 references that are in the map. First posted on arXiv 7 Feb 2022.

Builds on

30 of 97 references.

References · 30

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2016Deep Learning with Differential Privacycited by 2,682
  3. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  4. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  5. 2016Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learningcited by 1,509
  6. 2018A Reductions Approach to Fair Classificationcited by 585
  7. 2018Fairness Without Demographics in Repeated Loss Minimizationcited by 363
  8. 2020BERTweet: A pre-trained language model for English Tweetscited by 346
  9. 2020DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systemscited by 326
  10. 2021Dealing with Disagreements: Looking Beyond the Majority Vote in Subjective Annotationscited by 306
  11. 2019Human uncertainty makes classification more robustcited by 231
  12. 2021On Releasing Annotator-Level Labels and Information in Datasetscited by 123
18 more
  1. 2019Lessons from Archives: Strategies for Collecting Sociocultural Data in Machine Learningcited by 116
  2. 2021Do Datasets Have Politics? Disciplinary Values in Computer Vision Dataset Developmentcited by 85
  3. 2021Designing Toxic Content Classification for a Diversity of Perspectivescited by 63
  4. 2018Challenges for Toxic Comment Classification: An In-Depth Error Analysiscited by 55
  5. 2021Changing the World by Changing the Datacited by 51
  6. 2019Roles for Computing in Social Changecited by 49
  7. 2021Screen2Vec: Semantic Embedding of GUI Screens and GUI Componentscited by 41
  8. 2021Whose Opinions Matter? Perspective-aware Models to Identify Opinions of Hate Speech Victims in Abusive Language Detectioncited by 38
  9. 2017Society-in-the-Loop: Programming the Algorithmic Social Contractcited by 35
  10. 2018A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunitiescited by 28
  11. 2018Convolutional Neural Networks for Toxic Comment Classificationcited by 21
  12. 2020Keeping Community in the Loop: Understanding Wikipedia Stakeholder Values for Machine Learning-Based Systemscited by 16
  13. 2020PolicyKit: Building Governance in Online Communitiescited by 15
  14. 2021Personalized Embedding-based e-Commerce Recommendations at eBaycited by 12
  15. 2017Crowdsourcing Ground Truth for Medical Relation Extractioncited by 9
  16. 2018Capturing Ambiguity in Crowdsourcing Frame Disambiguationcited by 8
  17. 2021ESR: Ethics and Society Review of Artificial Intelligence Researchcited by 6
  18. 2014Crowdsourcing for Participatory Democracies: Efficient Elicitation of Social Choice Functionscited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 13; 2023: 35; 2024: 28; 2025: 15; 2026: 24.

115 papers.

Sort:

By area:

  1. 2023Whose Opinions Do Language Models Reflect?cited by 759
  2. 2023Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedbackcited by 743
  3. 2023LaMP: When Large Language Models Meet Personalizationcited by 425
  4. 2023Towards Measuring the Representation of Subjective Global Opinions in Language Modelscited by 424
  5. 2022Challenges and Strategies in Cross-Cultural NLPcited by 216
  6. 2024A Roadmap to Pluralistic Alignmentcited by 175
  7. 2022The 'Problem' of Human Label Variation: On Ground Truth in Data, Modeling and Evaluationcited by 133
  8. 2023We're Afraid Language Models Aren't Modeling Ambiguitycited by 124
  9. 2024Collective Constitutional AI: Aligning a Language Model with Public Inputcited by 120
  10. 2023Value Kaleidoscope: Engaging AI with Pluralistic Human Values, Rights, and Dutiescited by 97
  11. 2023Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitationscited by 96
  12. 2023When the Majority is Wrong: Modeling Annotator Disagreement for Subjective Taskscited by 89
38 more
  1. 2023The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practicecited by 86
  2. 2023Human Feedback is not Gold Standardcited by 82
  3. 2024PAD: Personalized Alignment of LLMs at Decoding-Timecited by 58
  4. 2023Bridging the Gap: A Survey on Integrating (Human) Feedback for Natural Language Generationcited by 55
  5. 2023The Past, Present and Better Future of Feedback Learning in Large Language Models for Subjective Human Preferences and Valuescited by 53
  6. 2023Everyone's Voice Matters: Quantifying Annotation Disagreement Using Demographic Informationcited by 52
  7. 2024Language Model Alignment in Multilingual Trolley Problemscited by 46
  8. 2024Beyond Preferences in AI Alignmentcited by 46
  9. 2023The Ecological Fallacy in Annotation: Modelling Human Label Variation goes beyond Sociodemographicscited by 45
  10. 2022Eliciting and Learning with Soft Labels from Every Annotatorcited by 43
  11. 2023You Are What You Annotate: Towards Better Models through Annotator Representationscited by 40
  12. 2025The Homogenizing Effect of Large Language Models on Human Expression and Thoughtcited by 35
  13. 2022Sensible AI: Re-imagining Interpretability and Explainability using Sensemaking Theorycited by 32
  14. 2023Conformal prediction under ambiguous ground truthcited by 31
  15. 2026HumanLM: Simulating Users with State Alignment Beats Response Imitationcited by 31
  16. 2022Understanding Practices, Challenges, and Opportunities for User-Engaged Algorithm Auditing in Industry Practicecited by 30
  17. 2024Farsight: Fostering Responsible AI Awareness During AI Application Prototypingcited by 28
  18. 2024LLM-GLOBE: A Benchmark Evaluating the Cultural Values Embedded in LLM Outputcited by 27
  19. 2024Position: Measure Dataset Diversity, Don't Just Claim Itcited by 26
  20. 2023Ground(less) Truth: A Causal Framework for Proxy Labels in Human-Algorithm Decision-Makingcited by 25
  21. 2025On Benchmarking Human-Like Intelligence in Machinescited by 24
  22. 2023What's the Meaning of Superhuman Performance in Today's NLU?cited by 22
  23. 2024Improving Context-Aware Preference Modeling for Language Modelscited by 22
  24. 2025SOP-Bench: Complex Industrial SOPs for Evaluating LLM Agentscited by 19
  25. 2024Wikibench: Community-Driven Data Curation for AI Evaluation on Wikipediacited by 18
  26. 2023Ethical Considerations for Responsible Data Curationcited by 17
  27. 2024Plurals: A System for Guiding LLMs Via Simulated Social Ensemblescited by 17
  28. 2022ToKen: Task Decomposition and Knowledge Infusion for Few-Shot Hate Speech Detectioncited by 15
  29. 2023Model Sketching: Centering Concepts in Early-Stage Machine Learning Model Designcited by 15
  30. 2024Does More Advice Help? The Effects of Second Opinions in AI-Assisted Decision Makingcited by 14
  31. 2023Learning Personalized Decision Support Policiescited by 13
  32. 2023Scaling Laws Do Not Scalecited by 13
  33. 2024Data for Mathematical Copilots: Better Ways of Presenting Proofs for Machine Learningcited by 11
  34. 2025From Replication to Redesign: Exploring Pairwise Comparisons for LLM-Based Peer Reviewcited by 11
  35. 2025Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Modelscited by 11
  36. 2023Same Same, But Different: Conditional Multi-Task Learning for Demographic-Specific Toxicity Detectioncited by 10
  37. 2023iLab at SemEval-2023 Task 11 Le-Wi-Di: Modelling Disagreement or Modelling Perspectives?cited by 10
  38. 2024Pareto-Optimal Learning from Preferences with Hidden Contextcited by 8

50 most cited of 115 All 115 (JSON)