mlsanityData to 7 Oct 2026

The illusion of artificial inclusion

arXiv 2401.08572 ↗First posted 16 Jan 2024cs.CYcited by 34

Not on the map. Why?

Abstract (arXiv)

Human participants play a central role in the development of modern artificial intelligence (AI) technology, in psychological science, and in user research. Recent advances in generative AI have attracted growing interest to the possibility of replacing human participants in these domains with AI surrogates. We survey several such "substitution proposals" to better understand the arguments for and against substituting human participants with modern generative AI. Our scoping review indicates that the recent wave of these proposals is motivated by goals such as reducing the costs of research and development work and increasing the diversity of collected data. However, these proposals ignore and ultimately conflict with foundational values of work with human participants: representation, inclusion, and understanding. This paper critically examines the principles and goals underlying human participation to help chart out paths for future work that truly centers and empowers participants.

Key facts. Cited by 34 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Jan 2024.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 15; 2025: 14; 2026: 5.

34 papers.

Sort:

By area:

  1. 2024The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Modelscited by 136
  2. 2025LLM Social Simulations Are a Promising Research Methodcited by 97
  3. 2024Steering Language Models with Game-Theoretic Solverscited by 28
  4. 2024Towards interactive evaluations for interaction harms in human-AI systemscited by 27
  5. 2024'Simulacrum of Stories': Examining Large Language Models as Qualitative Research Participantscited by 27
  6. 2024Real Risks of Fake Data: Synthetic Data, Diversity-Washing and Consent Circumventioncited by 21
  7. 2024LLM-Driven Robots Risk Enacting Discrimination, Violence, and Unlawful Actionscited by 21
  8. 2025Understanding the LLM-ification of CHI: Unpacking the Impact of LLMs at CHI through a Systematic Literature Reviewcited by 16
  9. 2024Designing AI-Agents with Personalities: A Psychometric Approachcited by 14
  10. 2025It's the Thought that Counts: Evaluating the Attempts of Frontier LLMs to Persuade on Harmful Topicscited by 13
  11. 2024Beyond the Binary: Capturing Diverse Preferences With Reward Regularizationcited by 11
  12. 2024Representation Bias in Political Sample Simulations with Large Language Modelscited by 10
22 more
  1. 2024Social Perception of Faces in a Vision-Language Modelcited by 10
  2. 2024The Future of Open Human Feedbackcited by 10
  3. 2024QuaLLM: An LLM-based Framework to Extract Quantitative Insights from Online Forumscited by 8
  4. 2025LIVS: A Pluralistic Alignment Dataset for Inclusive Public Spacescited by 8
  5. 2025Aligning LLM with human travel choices: a persona-based embedding learning approachcited by 7
  6. 2025Rigor in AI: Doing Rigorous AI Work Requires a Broader, Responsible AI-Informed Conception of Rigorcited by 7
  7. 2025Moloch's Bargain: Emergent Misalignment When LLMs Compete for Audiencescited by 7
  8. 2024AI and the Future of Digital Public Squarescited by 6
  9. 2025Do Role-Playing Agents Practice What They Preach? Belief-Behavior Consistency in LLM-Based Simulations of Human Trustcited by 6
  10. 2024How will advanced AI systems impact democracy?cited by 5
  11. 2025Stakeholder Participation for Responsible AI Development: Disconnects Between Guidance and Current Practicecited by 5
  12. 2025Adding LLMs to the psycholinguistic norming toolbox: A practical guide to getting the most out of human ratingscited by 3
  13. 2025Persona Alchemy: Designing, Evaluating, and Implementing Psychologically-Grounded LLM Agents for Diverse Stakeholder Representationcited by 2
  14. 2025AI LEGO: Scaffolding Cross-Functional Collaboration in Industrial Responsible AI Practices during Early Design Stagescited by 1
  15. 2025What Would an LLM Do? Evaluating Large Language Models for Policymaking to Alleviate Homelessnesscited by 1
  16. 2026Plans They Abandon, Reports They Author: The Narrative Layer of Autonomous Agentscited by 1
  17. 2024Connecting Algorithmic Fairness to Quality Dimensions in Machine Learning in Official Statistics and Survey Productioncited by 0
  18. 2025Survey Transfer Learning: Recycling Data with Silicon Responsescited by 0
  19. 2026Who Does What? Archetypes of Roles Assigned to LLMs During Human-AI Decision-Makingcited by 0
  20. 2026Mechanism Plausibility in Generative Agent-Based Modelingcited by 0
  21. 2026VERA-MH: Validation of Ethical and Responsible AI in Mental Healthcited by 0
  22. 2026From Demographics to Survey Anchors: Evaluating LLM Agents for Modeling Retirement Attitudescited by 0