# Human-AI Interactions and Societal Pitfalls

arXiv: https://arxiv.org/abs/2309.10448 · HTML: https://mlsanity.com/paper/2309.10448/

## 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 (most cited first, 18 of 18)

- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Fine-Tuning Language Models from Human Preferences](https://mlsanity.com/paper/1909.08593/) (2019)
- [An Explanation of In-context Learning as Implicit Bayesian Inference](https://mlsanity.com/paper/2111.02080/) (2021)
- [Using cognitive psychology to understand GPT-3](https://mlsanity.com/paper/2206.14576/) (2022)
- [The Curse of Recursion: Training on Generated Data Makes Models Forget](https://mlsanity.com/paper/2305.17493/) (2023)
- [Emergent Analogical Reasoning in Large Language Models](https://mlsanity.com/paper/2212.09196/) (2022)
- [Does Writing with Language Models Reduce Content Diversity?](https://mlsanity.com/paper/2309.05196/) (2023)
- [The political ideology of conversational AI: Converging evidence on ChatGPT's pro-environmental, left-libertarian orientation](https://mlsanity.com/paper/2301.01768/) (2023)
- [GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models](https://mlsanity.com/paper/2303.10130/) (2023)
- [Co-Writing with Opinionated Language Models Affects Users' Views](https://mlsanity.com/paper/2302.00560/) (2023)
- [How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility](https://mlsanity.com/paper/1710.11214/) (2017)
- [Picking on the Same Person: Does Algorithmic Monoculture lead to Outcome Homogenization?](https://mlsanity.com/paper/2211.13972/) (2022)
- [Why Do Pretrained Language Models Help in Downstream Tasks? An Analysis of Head and Prompt Tuning](https://mlsanity.com/paper/2106.09226/) (2021)
- [Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using Natural Language](https://mlsanity.com/paper/2210.15157/) (2022)
- [Bayesian GAN](https://mlsanity.com/paper/1705.09558/) (2017)
- [Interacting with next-phrase suggestions: How suggestion systems aid and influence the cognitive processes of writing](https://mlsanity.com/paper/2208.00636/) (2022)
- [Algorithmic Assistance with Recommendation-Dependent Preferences](https://mlsanity.com/paper/2208.07626/) (2022)
- [Algorithmic Decision-Making Safeguarded by Human Knowledge](https://mlsanity.com/paper/2211.11028/) (2022)

## Built on by (most cited first, 9 of 9)

- [Pricing and Competition for Generative AI](https://mlsanity.com/paper/2411.02661/) (2024)
- [The Economics of AI Foundation Models: Openness, Competition, and Governance](https://mlsanity.com/paper/2510.15200/) (2025)
- [Routing, Cascades, and User Choice for LLMs](https://mlsanity.com/paper/2602.09902/) (2026)
- [Human-AI Productivity Paradoxes: Modeling the Interplay of Skill, Effort, and AI Assistance](https://mlsanity.com/paper/2605.11350/) (2026)
- [The Variance Paradox: How AI Reduces Diversity but Increases Novelty](https://mlsanity.com/paper/2508.19264/) (2025)
- [Interrogating Design Homogenization in Web Vibe Coding](https://mlsanity.com/paper/2603.13036/) (2026)
- [Profiles of AI Dependency: A Latent Class Analysis of Filipino Students' Academic Competencies](https://mlsanity.com/paper/2604.27349/) (2026)
- [When to Ask a Question: Understanding Communication Strategies in Generative AI Tools](https://mlsanity.com/paper/2605.11240/) (2026)
- [Right-Sizing Communication and Recommendation Set Size in AI-Assisted Search](https://mlsanity.com/paper/2605.23944/) (2026)

## 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.