# Who Benefits from AI? Self-Selection, Skill Gap, and the Hidden Costs of AI Feedback

arXiv: https://arxiv.org/abs/2409.18660 · HTML: https://mlsanity.com/paper/2409.18660/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 94 references that are in the map.
- First posted on arXiv 27 Sep 2024.

## Builds on (most cited first, 6 of 6)

- [Generalized Random Forests](https://mlsanity.com/paper/1610.01271/) (2016)
- [An Overview of Catastrophic AI Risks](https://mlsanity.com/paper/2306.12001/) (2023)
- [Generative artificial intelligence enhances creativity but reduces the diversity of novel content](https://mlsanity.com/paper/2312.00506/) (2023)
- [Algorithmic Monoculture and Social Welfare](https://mlsanity.com/paper/2101.05853/) (2021)
- [Picking on the Same Person: Does Algorithmic Monoculture lead to Outcome Homogenization?](https://mlsanity.com/paper/2211.13972/) (2022)
- [Superhuman Artificial Intelligence Can Improve Human Decision Making by Increasing Novelty](https://mlsanity.com/paper/2303.07462/) (2023)

## Abstract (arXiv)

Feedback from artificial intelligence (AI) is increasingly easy to access and research has already established that people learn from it. But individuals choose when and how to seek such feedback, and more engaged and motivated individuals may seek it more, creating an illusion of effectiveness that masks self-selection. We investigate how the endogenous choice to seek AI feedback shapes both individual learning and collective outcomes. Using data from over five years and 52,000 individuals on an online chess platform, we show that motivated and higher-skilled individuals self-select into AI feedback use-and use it more productively. This self-selection creates an illusion of AI effectiveness: apparent learning gains disappear once endogenous motivation is accounted for. This same selection mechanism drives two population-level consequences. Because motivated, higher-skilled individuals benefit disproportionately, AI access widens the skill gap. And because individuals exposed to centralized AI feedback converge on common input from a centralized AI source, intellectual diversity declines. Leveraging 42 platform-level natural experiments, we show this diversity reduction is causal. Self-selection into AI use thus connects individual-level learning dynamics to collective-level consequences-a micro-macro linkage with implications for organizational learning, human capital development, and the design of AI-augmented work.