When Incentives Backfire, Data Stops Being Human
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866915332494458880 |
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| author | Santy, Sebastin Bhattacharya, Prasanta Ribeiro, Manoel Horta Allen, Kelsey Oh, Sewoong |
| author_facet | Santy, Sebastin Bhattacharya, Prasanta Ribeiro, Manoel Horta Allen, Kelsey Oh, Sewoong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_07732 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | When Incentives Backfire, Data Stops Being Human Santy, Sebastin Bhattacharya, Prasanta Ribeiro, Manoel Horta Allen, Kelsey Oh, Sewoong Computers and Society Artificial Intelligence Computation and Language Computer Vision and Pattern Recognition Human-Computer Interaction Machine Learning 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. |
| title | When Incentives Backfire, Data Stops Being Human |
| topic | Computers and Society Artificial Intelligence Computation and Language Computer Vision and Pattern Recognition Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2502.07732 |