mlsanityData to 14 Oct 2026

Disentangling Perceptions of Offensiveness: Cultural and Moral Correlates

arXiv 2312.06861 ↗First posted 11 Dec 2023cs.CY cs.CLcited by 26

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Abstract (arXiv)

Perception of offensiveness is inherently subjective, shaped by the lived experiences and socio-cultural values of the perceivers. Recent years have seen substantial efforts to build AI-based tools that can detect offensive language at scale, as a means to moderate social media platforms, and to ensure safety of conversational AI technologies such as ChatGPT and Bard. However, existing approaches treat this task as a technical endeavor, built on top of data annotated for offensiveness by a global crowd workforce without any attention to the crowd workers' provenance or the values their perceptions reflect. We argue that cultural and psychological factors play a vital role in the cognitive processing of offensiveness, which is critical to consider in this context. We re-frame the task of determining offensiveness as essentially a matter of moral judgment -- deciding the boundaries of ethically wrong vs. right language within an implied set of socio-cultural norms. Through a large-scale cross-cultural study based on 4309 participants from 21 countries across 8 cultural regions, we demonstrate substantial cross-cultural differences in perceptions of offensiveness. More importantly, we find that individual moral values play a crucial role in shaping these variations: moral concerns about Care and Purity are significant mediating factors driving cross-cultural differences. These insights are of crucial importance as we build AI models for the pluralistic world, where the values they espouse should aim to respect and account for moral values in diverse geo-cultural contexts.

Key facts. Cited by 26 papers in the map (data through 14 Oct 2026). Builds on 36 of its 101 references that are in the map. First posted on arXiv 11 Dec 2023.

Builds on

36 of 101 references.

References · 36

  1. 2022Training language models to follow instructions with human feedbackcited by 15,971
  2. 2022Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackcited by 3,720
  3. 2020Extracting Training Data from Large Language Modelscited by 2,189
  4. 2022Improving alignment of dialogue agents via targeted human judgementscited by 531
  5. 2020Artificial Intelligence, Values and Alignmentcited by 448
  6. 2021Dealing with Disagreements: Looking Beyond the Majority Vote in Subjective Annotationscited by 314
  7. 2018Large Scale Crowdsourcing and Characterization of Twitter Abusive Behaviorcited by 270
  8. 2021Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detectioncited by 264
  9. 2021Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language Modelscited by 232
  10. 2023Red-Teaming Large Language Models using Chain of Utterances for Safety-Alignmentcited by 223
  11. 2022Challenges and Strategies in Cross-Cultural NLPcited by 218
  12. 2016Ex Machina: Personal Attacks Seen at Scalecited by 210
24 more
  1. 2019Are We Modeling the Task or the Annotator? An Investigation of Annotator Bias in Natural Language Understanding Datasetscited by 175
  2. 2021The Values Encoded in Machine Learning Researchcited by 172
  3. 2021Alignment of Language Agentscited by 168
  4. 2020Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructurecited by 138
  5. 2021Toward a Perspectivist Turn in Ground Truthing for Predictive Computingcited by 130
  6. 2021On Releasing Annotator-Level Labels and Information in Datasetscited by 126
  7. 2019Towards a Critical Race Methodology in Algorithmic Fairnesscited by 125
  8. 2023Explore, Establish, Exploit: Red Teaming Language Models from Scratchcited by 114
  9. 2023NLPositionality: Characterizing Design Biases of Datasets and Modelscited by 94
  10. 2023When the Majority is Wrong: Modeling Annotator Disagreement for Subjective Taskscited by 90
  11. 2023Moral Foundations of Large Language Modelscited by 85
  12. 2023AI model GPT-3 (dis)informs us better than humanscited by 78
  13. 2022Is Your Toxicity My Toxicity? Exploring the Impact of Rater Identity on Toxicity Annotationcited by 69
  14. 2022Handling Bias in Toxic Speech Detection: A Surveycited by 61
  15. 2022CrowdWorkSheets: Accounting for Individual and Collective Identities Underlying Crowdsourced Dataset Annotationcited by 59
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  17. 2023Everyone's Voice Matters: Quantifying Annotation Disagreement Using Demographic Informationcited by 52
  18. 2022How Different Groups Prioritize Ethical Values for Responsible AIcited by 49
  19. 2020Confronting Abusive Language Online: A Survey from the Ethical and Human Rights Perspectivecited by 43
  20. 2020Between Subjectivity and Imposition: Power Dynamics in Data Annotation for Computer Visioncited by 25
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 7; 2025: 9; 2026: 10.

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  1. 2025AI Alignment at Your Discretioncited by 20
  2. 2026Beyond Consensus: Perspectivist Modeling and Evaluation of Annotator Disagreement in NLPcited by 13
  3. 2024Evaluating Moral Beliefs across LLMs through a Pluralistic Frameworkcited by 11
  4. 2025Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Modelscited by 11
  5. 2024Detectors for Safe and Reliable LLMs: Implementations, Uses, and Limitationscited by 10
  6. 2025Modeling Annotator Disagreement with Demographic-Aware Experts and Synthetic Perspectivescited by 10
  7. 2024Re-examining Sexism and Misogyny Classification with Annotator Attitudescited by 8
  8. 2025Value Compass Benchmarks: A Platform for Fundamental and Validated Evaluation of LLMs Valuescited by 7
  9. 2025Un-Straightening Generative AI: How Queer Artists Surface and Challenge the Normativity of Generative AI Modelscited by 6
  10. 2026JailNewsBench: Multi-Lingual and Regional Benchmark for Fake News Generation under Jailbreak Attackscited by 6
  11. 2026Self-Explaining Hate Speech Detection with Moral Rationalescited by 5
  12. 2024Adaptable Moral Stances of Large Language Models on Sexist Content: Implications for Society and Gender Discoursecited by 4
14 more
  1. 2024Insights on Disagreement Patterns in Multimodal Safety Perception across Diverse Rater Groupscited by 4
  2. 2025Mind the Gesture: Evaluating AI Sensitivity to Culturally Offensive Non-Verbal Gesturescited by 4
  3. 2024Trustworthy and Responsible AI for Human-Centric Autonomous Decision-Making Systemscited by 3
  4. 2026Reflect: Transparent Principle-Guided Reasoning for Constitutional Alignment at Scalecited by 2
  5. 2026The Consensus Trap: Dissecting Subjectivity and the "Ground Truth" Illusion in Data Annotationcited by 2
  6. 2024Ethics Whitepaper: Whitepaper on Ethical Research into Large Language Modelscited by 1
  7. 2025That is Unacceptable: the Moral Foundations of Cancelingcited by 1
  8. 2025Language, Culture, and Ideology: Personalizing Offensiveness Detection in Political Tweets with Reasoning LLMscited by 1
  9. 2026Labels have Human Values: Value Calibration of Subjective Taskscited by 1
  10. 2025Foundation Models at Work: Fine-Tuning for Fairness in Algorithmic Hiringcited by 0
  11. 2026Learning Who Disagrees: Demographic Importance Weighting for Modeling Annotator Distributions with DiADEMcited by 0
  12. 2026The Ghost Annotator: a Framework to Explore Human Label Variation in Content Moderation through Conformal Predictioncited by 0
  13. 2026Epistemic Injustice in Language Models: An Audit of Pretraining Filters and Guardrailscited by 0
  14. 2026How Ethos and Pathos Appeals Resonate in Reader Interpretations of Social Media Messagescited by 0