Mecha-nudges for Machines

Abstract:AI agents are becoming active decision-makers on the Internet. As they make decisions in the same environments as humans, the environments themselves can change to influence them. We call this $\textit{mecha-nudging}$: changes to how choices are presented that systematically influence AI agents without materially degrading the decision environment for humans. To measure this phenomenon, we combine two frameworks---Bayesian persuasion from economics and $\mathcal{V}$-usable information from computer science---to get a common unit (bits) for quantifying how environments change across a wide range of interventions, contexts, and models. We apply this framework to over six million Etsy listings and find that, after ChatGPT's release, listings contain significantly more machine-usable information for predicting agent curation decisions, increasing by 0.143 bits out of a maximum possible increase of 0.355. This shift is robust across prompts, token choices, labeling models, and fine-tuning architectures; absent in a regulated-text placebo; and far larger than the effect of generic LLM rewriting. In contrast, a human study finds little to no change in human-usable information. Our results provide the first large-scale evidence that systematic mecha-nudging is already occurring in the wild, but going unnoticed.

Artificial Intelligence (cs.AI)

arXiv:2603.23433 [cs.AI]

(or arXiv:2603.23433v4 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2603.23433 Focus to learn more arXiv-issued DOI via DataCite

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