Incentives shape how humans co-create with generative AI
This addresses the issue of AI homogenizing creative outputs for users in collaborative settings, offering a practical solution through incentive design, though it is incremental as it builds on existing concerns about AI's impact on diversity.
The study tackled the problem of generative AI reducing collective diversity in creative tasks by showing through a randomized control trial that incentivizing originality over quality leads to more diverse writing, with participants using AI more selectively for brainstorming and editing rather than verbatim suggestions.
Generative AI is quickly becoming an integral part of people's everyday workflows. Early evidence has shown that while generative AI can increase individual-level productivity, it does so at the cost of collective diversity, potentially narrowing the set of ideas and perspectives produced. Our research stands in contrast to this concern: through a pre-registered randomized control trial, we show that incentives mediate AI's homogenizing force in a creative writing task where participants can use AI interactively. Participants rewarded for originality relative to peers produce collectively more diverse writing than those rewarded for quality alone. This divergence is driven not by abandoning AI, but by how participants use it: those incentivized for originality incorporate fewer AI suggestions verbatim, relying on the model more selectively for brainstorming, proofreading, and targeted edits. Our results reveal that the effects of generative AI depend not only on the technology itself, but also the behavioral strategies and incentive structures surrounding its use.