# The illusion of artificial inclusion

arXiv: https://arxiv.org/abs/2401.08572 · HTML: https://mlsanity.com/paper/2401.08572/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2024.

## Built on by (most cited first, 30 of 34)

- [The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models](https://mlsanity.com/paper/2404.16019/) (2024)
- [LLM Social Simulations Are a Promising Research Method](https://mlsanity.com/paper/2504.02234/) (2025)
- [Towards interactive evaluations for interaction harms in human-AI systems](https://mlsanity.com/paper/2405.10632/) (2024)
- [Steering Language Models with Game-Theoretic Solvers](https://mlsanity.com/paper/2402.01704/) (2024)
- ['Simulacrum of Stories': Examining Large Language Models as Qualitative Research Participants](https://mlsanity.com/paper/2409.19430/) (2024)
- [LLM-Driven Robots Risk Enacting Discrimination, Violence, and Unlawful Actions](https://mlsanity.com/paper/2406.08824/) (2024)
- [Real Risks of Fake Data: Synthetic Data, Diversity-Washing and Consent Circumvention](https://mlsanity.com/paper/2405.01820/) (2024)
- [Understanding the LLM-ification of CHI: Unpacking the Impact of LLMs at CHI through a Systematic Literature Review](https://mlsanity.com/paper/2501.12557/) (2025)
- [Designing AI-Agents with Personalities: A Psychometric Approach](https://mlsanity.com/paper/2410.19238/) (2024)
- [It's the Thought that Counts: Evaluating the Attempts of Frontier LLMs to Persuade on Harmful Topics](https://mlsanity.com/paper/2506.02873/) (2025)
- [Beyond the Binary: Capturing Diverse Preferences With Reward Regularization](https://mlsanity.com/paper/2412.03822/) (2024)
- [Representation Bias in Political Sample Simulations with Large Language Models](https://mlsanity.com/paper/2407.11409/) (2024)
- [Social Perception of Faces in a Vision-Language Model](https://mlsanity.com/paper/2408.14435/) (2024)
- [The Future of Open Human Feedback](https://mlsanity.com/paper/2408.16961/) (2024)
- [QuaLLM: An LLM-based Framework to Extract Quantitative Insights from Online Forums](https://mlsanity.com/paper/2405.05345/) (2024)
- [LIVS: A Pluralistic Alignment Dataset for Inclusive Public Spaces](https://mlsanity.com/paper/2503.01894/) (2025)
- [Moloch's Bargain: Emergent Misalignment When LLMs Compete for Audiences](https://mlsanity.com/paper/2510.06105/) (2025)
- [Aligning LLM with human travel choices: a persona-based embedding learning approach](https://mlsanity.com/paper/2505.19003/) (2025)
- [Rigor in AI: Doing Rigorous AI Work Requires a Broader, Responsible AI-Informed Conception of Rigor](https://mlsanity.com/paper/2506.14652/) (2025)
- [AI and the Future of Digital Public Squares](https://mlsanity.com/paper/2412.09988/) (2024)
- [Do Role-Playing Agents Practice What They Preach? Belief-Behavior Consistency in LLM-Based Simulations of Human Trust](https://mlsanity.com/paper/2507.02197/) (2025)
- [How will advanced AI systems impact democracy?](https://mlsanity.com/paper/2409.06729/) (2024)
- [Stakeholder Participation for Responsible AI Development: Disconnects Between Guidance and Current Practice](https://mlsanity.com/paper/2506.09873/) (2025)
- [Adding LLMs to the psycholinguistic norming toolbox: A practical guide to getting the most out of human ratings](https://mlsanity.com/paper/2509.14405/) (2025)
- [Plans They Abandon, Reports They Author: The Narrative Layer of Autonomous Agents](https://mlsanity.com/paper/2609.12205/) (2026)
- [Persona Alchemy: Designing, Evaluating, and Implementing Psychologically-Grounded LLM Agents for Diverse Stakeholder Representation](https://mlsanity.com/paper/2505.18351/) (2025)
- [From Demographics to Survey Anchors: Evaluating LLM Agents for Modeling Retirement Attitudes](https://mlsanity.com/paper/2605.16303/) (2026)
- [AI LEGO: Scaffolding Cross-Functional Collaboration in Industrial Responsible AI Practices during Early Design Stages](https://mlsanity.com/paper/2505.10300/) (2025)
- [What Would an LLM Do? Evaluating Large Language Models for Policymaking to Alleviate Homelessness](https://mlsanity.com/paper/2509.03827/) (2025)
- [Connecting Algorithmic Fairness to Quality Dimensions in Machine Learning in Official Statistics and Survey Production](https://mlsanity.com/paper/2402.09328/) (2024)

## 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.