Position: When Incentives Backfire, Data Stops Being Human
Sebastin Santy, Prasanta Bhattacharya, Manoel Horta Ribeiro, Kelsey R Allen, Sewoong Oh
Abstract
Progress in AI has relied on human-generated data, from annotator marketplaces to the wider Internet. However, the widespread use of large language models now threatens the quality and integrity of human-generated data on these very platforms. We argue that this issue goes beyond the immediate challenge of filtering AI-generated content -- it reveals deeper flaws in how data collection systems are designed. Existing systems often prioritize speed, scale, and efficiency at the cost of intrinsic human motivation, leading to declining engagement and data quality. We propose that rethinking data collection systems to align with contributors' intrinsic motivations -- rather than relying solely on external incentives -- can help sustain high-quality data sourcing at scale while maintaining contributor trust and long-term participation.
BibTeX
@inproceedings{
santy2025position,
title={Position: When Incentives Backfire, Data Stops Being Human},
author={Sebastin Santy and Prasanta Bhattacharya and Manoel Horta Ribeiro and Kelsey R Allen and Sewoong Oh},
booktitle={Forty-second International Conference on Machine Learning Position Paper Track},
year={2025},
url={https://openreview.net/forum?id=4UhTWPwVke}
}