When Incentives Backfire, Data Stops Being Human

TL;DR

The paper discusses how current AI data collection systems undermine human motivation, leading to declining data quality, and advocates for redesigning these systems to align with intrinsic human motivations for sustainable data sourcing.

Contribution

It highlights the flaws in existing data collection systems and proposes a new approach that emphasizes intrinsic motivation to improve data quality and contributor engagement.

Findings

Current incentive structures reduce human motivation and data quality.

Aligning data collection with intrinsic motivations can sustain contributor engagement.

Rethinking system design is essential for long-term high-quality data sourcing.

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.