mlsanityData to 14 Oct 2026

Rater Cohesion and Quality from a Vicarious Perspective

arXiv 2408.08411 ↗First posted 15 Aug 2024cs.CLcited by 8

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Abstract (arXiv)

Human feedback is essential for building human-centered AI systems across domains where disagreement is prevalent, such as AI safety, content moderation, or sentiment analysis. Many disagreements, particularly in politically charged settings, arise because raters have opposing values or beliefs. Vicarious annotation is a method for breaking down disagreement by asking raters how they think others would annotate the data. In this paper, we explore the use of vicarious annotation with analytical methods for moderating rater disagreement. We employ rater cohesion metrics to study the potential influence of political affiliations and demographic backgrounds on raters' perceptions of offense. Additionally, we utilize CrowdTruth's rater quality metrics, which consider the demographics of the raters, to score the raters and their annotations. We study how the rater quality metrics influence the in-group and cross-group rater cohesion across the personal and vicarious levels.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 21 of its 44 references that are in the map. First posted on arXiv 15 Aug 2024.

Builds on

21 of 44 references.

References · 21

  1. 2022Training language models to follow instructions with human feedbackcited by 15,971
  2. 2023Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedbackcited by 759
  3. 2021Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detectioncited by 264
  4. 2019Racial Bias in Hate Speech and Abusive Language Detection Datasetscited by 209
  5. 2019Are We Modeling the Task or the Annotator? An Investigation of Annotator Bias in Natural Language Understanding Datasetscited by 175
  6. 2017Interactive Learning from Policy-Dependent Human Feedbackcited by 158
  7. 2018Reducing Gender Bias in Abusive Language Detectioncited by 152
  8. 2021Toward a Perspectivist Turn in Ground Truthing for Predictive Computingcited by 130
  9. 2021On Releasing Annotator-Level Labels and Information in Datasetscited by 126
  10. 2022Is Your Toxicity My Toxicity? Exploring the Impact of Rater Identity on Toxicity Annotationcited by 69
  11. 2021Designing Toxic Content Classification for a Diversity of Perspectivescited by 65
  12. 2023When Do Annotator Demographics Matter? Measuring the Influence of Annotator Demographics with the POPQUORN Datasetcited by 52
9 more
  1. 2017Like trainer, like bot? Inheritance of bias in algorithmic content moderationcited by 35
  2. 2017Argument Strength is in the Eye of the Beholder: Audience Effects in Persuasioncited by 35
  3. 2021Cross-replication Reliability -- An Empirical Approach to Interpreting Inter-rater Reliabilitycited by 31
  4. 2018CrowdTruth 2.0: Quality Metrics for Crowdsourcing with Disagreementcited by 27
  5. 2023From Dogwhistles to Bullhorns: Unveiling Coded Rhetoric with Language Modelscited by 26
  6. 2020We Don't Speak the Same Language: Interpreting Polarization through Machine Translationcited by 23
  7. 2023Vicarious Offense and Noise Audit of Offensive Speech Classifiers: Unifying Human and Machine Disagreement on What is Offensivecited by 16
  8. 2023GRASP: A Disagreement Analysis Framework to Assess Group Associations in Perspectivescited by 16
  9. 2023Intersectionality in Conversational AI Safety: How Bayesian Multilevel Models Help Understand Diverse Perceptions of Safetycited by 8

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 5.

8 papers.

  1. 2025How Inclusively do LMs Perceive Social and Moral Norms?cited by 3
  2. 2025Hope vs. Hate: Understanding User Interactions with LGBTQ+ News Content in Mainstream US News Media through the Lens of Hope Speechcited by 2
  3. 2025LPI-RIT at LeWiDi-2025: Improving Distributional Predictions via Metadata and Loss Reweighting with DisCocited by 2
  4. 2026Structuring the Space of Sociotechnical Alignmentcited by 2
  5. 2026Learning Who Disagrees: Demographic Importance Weighting for Modeling Annotator Distributions with DiADEMcited by 0
  6. 2026Investigating Vaccine Buyer's Remorse: Post-Vaccination Decision Regret in COVID-19 Social Media Using Politically Diverse Human Annotationcited by 0
  7. 2026Improving Reproducibility in Evaluation through Multi-Level Annotator Modelingcited by 0
  8. 2026Thesis Proposal: Toward a Human-Centered and Perspective-Aware Framework for Reproducible ML Evaluation and AI Alignmentcited by 0