# AI and the Dynamic Supply of Training Data

arXiv: https://arxiv.org/abs/2404.18445 · HTML: https://mlsanity.com/paper/2404.18445/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 54 references that are in the map.
- First posted on arXiv 29 Apr 2024.

## Builds on (most cited first, 13 of 13)

- [Large image datasets: A pyrrhic win for computer vision?](https://mlsanity.com/paper/2006.16923/) (2020)
- [Data-centric Artificial Intelligence: A Survey](https://mlsanity.com/paper/2303.10158/) (2023)
- [Generative artificial intelligence enhances creativity but reduces the diversity of novel content](https://mlsanity.com/paper/2312.00506/) (2023)
- [Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data](https://mlsanity.com/paper/2404.01413/) (2024)
- [Foundation Models and Fair Use](https://mlsanity.com/paper/2303.15715/) (2023)
- [The Data Provenance Initiative: A Large Scale Audit of Dataset Licensing & Attribution in AI](https://mlsanity.com/paper/2310.16787/) (2023)
- [Online Continual Learning with Natural Distribution Shifts: An Empirical Study with Visual Data](https://mlsanity.com/paper/2108.09020/) (2021)
- [Algorithmic progress in language models](https://mlsanity.com/paper/2403.05812/) (2024)
- [An Economic Solution to Copyright Challenges of Generative AI](https://mlsanity.com/paper/2404.13964/) (2024)
- [Generative AI and Copyright: A Dynamic Perspective](https://mlsanity.com/paper/2402.17801/) (2024)
- [When Is Parallel Trends Sensitive to Functional Form?](https://mlsanity.com/paper/2010.04814/) (2020)
- [Time and the Value of Data](https://mlsanity.com/paper/2203.09118/) (2022)
- [Time Dependency, Data Flow, and Competitive Advantage](https://mlsanity.com/paper/2203.09128/) (2022)

## Abstract (arXiv)

Artificial intelligence (AI) systems rely heavily on human-generated data, yet the people behind that data are often overlooked. Human behavior can play a major role in AI training datasets, be it in limiting access to existing works or in deciding which types of new works to create or whether to create any at all. We examine creators' behavioral change when their works become training data for commercial AI. Specifically, we focus on contributors on Unsplash, a popular stock image platform with about 6 million high-quality photos and illustrations. In the summer of 2020, Unsplash launched a research program and released a dataset of 25,000 images for commercial AI use. We study contributors' reactions, comparing contributors whose works were included in this dataset to contributors whose works were not. Our results suggest that treated contributors left the platform at a higher-than-usual rate and substantially slowed down the rate of new uploads. Professional photographers and more heavily affected users had a stronger reaction than amateurs and less affected users. We also show that affected users changed the variety and novelty of contributions to the platform, which can potentially lead to lower-quality AI outputs in the long run. Our findings highlight a critical trade-off: the drive to expand AI capabilities versus the incentives of those producing training data. We conclude with policy proposals, including dynamic compensation schemes and structured data markets, to realign incentives at the data frontier.