mlsanityData to 14 Oct 2026

Human-AI Interactions and Societal Pitfalls

arXiv 2309.10448 ↗First posted 19 Sep 2023cs.AI cs.HC econ.GNcited by 9

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Abstract (arXiv)

When working with generative artificial intelligence (AI), users may see productivity gains, but the AI-generated content may not match their preferences exactly. To study this effect, we introduce a Bayesian framework in which heterogeneous users choose how much information to share with the AI, facing a trade-off between output fidelity and communication cost. We show that the interplay between these individual-level decisions and AI training may lead to societal challenges. Outputs may become more homogenized, especially when the AI is trained on AI-generated content, potentially triggering a homogenization death spiral. And any AI bias may propagate to become societal bias. A solution to the homogenization and bias issues is to reduce human-AI interaction frictions and enable users to flexibly share information, leading to personalized outputs without sacrificing productivity.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 18 of its 64 references that are in the map. First posted on arXiv 19 Sep 2023.

Builds on

18 of 64 references.

References · 18

  1. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,234
  2. 2019Fine-Tuning Language Models from Human Preferencescited by 2,150
  3. 2021An Explanation of In-context Learning as Implicit Bayesian Inferencecited by 938
  4. 2022Using cognitive psychology to understand GPT-3cited by 398
  5. 2023The Curse of Recursion: Training on Generated Data Makes Models Forgetcited by 356
  6. 2022Emergent Analogical Reasoning in Large Language Modelscited by 240
  7. 2023Does Writing with Language Models Reduce Content Diversity?cited by 192
  8. 2023The political ideology of conversational AI: Converging evidence on ChatGPT's pro-environmental, left-libertarian orientationcited by 189
  9. 2023GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Modelscited by 176
  10. 2023Co-Writing with Opinionated Language Models Affects Users' Viewscited by 148
  11. 2017How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utilitycited by 120
  12. 2022Picking on the Same Person: Does Algorithmic Monoculture lead to Outcome Homogenization?cited by 91
6 more
  1. 2021Why Do Pretrained Language Models Help in Downstream Tasks? An Analysis of Head and Prompt Tuningcited by 82
  2. 2022Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using Natural Languagecited by 82
  3. 2017Bayesian GANcited by 32
  4. 2022Interacting with next-phrase suggestions: How suggestion systems aid and influence the cognitive processes of writingcited by 27
  5. 2022Algorithmic Assistance with Recommendation-Dependent Preferencescited by 12
  6. 2022Algorithmic Decision-Making Safeguarded by Human Knowledgecited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 6.

9 papers.

  1. 2024Pricing and Competition for Generative AIcited by 8
  2. 2025The Economics of AI Foundation Models: Openness, Competition, and Governancecited by 4
  3. 2026Routing, Cascades, and User Choice for LLMscited by 2
  4. 2026Human-AI Productivity Paradoxes: Modeling the Interplay of Skill, Effort, and AI Assistancecited by 2
  5. 2025The Variance Paradox: How AI Reduces Diversity but Increases Noveltycited by 1
  6. 2026Interrogating Design Homogenization in Web Vibe Codingcited by 1
  7. 2026Profiles of AI Dependency: A Latent Class Analysis of Filipino Students' Academic Competenciescited by 0
  8. 2026When to Ask a Question: Understanding Communication Strategies in Generative AI Toolscited by 0
  9. 2026Right-Sizing Communication and Recommendation Set Size in AI-Assisted Searchcited by 0