Replication Report: Corrupted by Algorithms? How AI-generated And Human-written Advice Shape (Dis)Honesty

Lachlan Deer, Adithya Krishna and Lyla Zhang

No 212, I4R Discussion Paper Series from The Institute for Replication (I4R)

Abstract: Leib et al. (2024) examine how artificial intelligence (AI) generated advice affects dishonesty compared to equivalent human advice in a laboratory experiment. In their preferred empirical specification, the authors report that dishonesty-promoting advice increases dishonest behavior by approximately 15% compared to a baseline without advice, while honesty-promoting advice has no significant effect. Additionally, they find that algorithmic transparency - disclosing whether advice comes from AI or humans - does not affect behavior. We computationally reproduce the main results of the paper using the same procedures and original data. Our results confirm the sign, magnitude, and statistical significance of the authors' reported estimates across each of their main findings. Additional robustness checks show that the significance of the results remains stable under alternative specifications and methodological choices.

Keywords: artificial intelligence; dishonesty; laboratory experiment; computational reproducibility (search for similar items in EconPapers)
JEL-codes: C91 D01 D91 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:i4rdps:212

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