Human Learning about AI (with Bnaya Dreyfuss) - Extended abstract at EC'25
Abstract: We study how humans form expectations about the performance of artificial intelligence (AI) and consequences for AI adoption. Our main hypothesis is that people project human-relevant task features onto AI. People then over-infer from AI failures on human-easy tasks, and from AI successes on human-difficult tasks. Lab experiments provide strong evidence for projection of human difficulty onto AI, predictably distorting subjects’ expectations. Resulting adoption can be sub-optimal, as failing human-easy tasks need not imply poor overall performance in the case of AI. A field experiment with an AI giving parenting advice shows evidence for projection of human textual similarity. Users strongly infer from answers that are equally uninformative but less humanly-similar to expected answers, significantly reducing trust and engagement. Results suggest AI “anthropomorphism” can backfire by increasing projection and de-aligning human expectations and AI performance.
Media Coverage:
Harvard Horizons "Ted Talk style" presentation
[NEW!] (Replaces older version titled "Signaling Universalism")
Strategic Slanting (reject and resubmit at Journal of the European Economic Association)
Abstract: How do social image concerns affect displayed group preferences? I provide experimental evidence that decisions makers (DMs) engage in strategic slanting: they distort their public behavior towards their perception of their audience’s preference to induce audiences to act prosocially towards them. DMs play a universalism game, dividing money between an in-group and an out-group member, where I manipulate the existence and identity—in-group or out-group—of an audience with whom the DM expects downstream interactions. Across three types of interactions—prisoner’s dilemma, dictator game, and no game—DMs act significantly more universalist when facing out-group audiences and in-group audiences perceived as universalist, while they act more communal when facing in-group audiences perceived as communal. Shocking the perceived audience’s strength of group identity, I find that social cues inform DMs on the direction of their slanting, which is performed
only when DMs believe it can affect their audience’s prosociality. Findings are consistent with a model where DMs strategically display preference alignment to audiences who are altruistic towards like-minded people. Slanting-driven alignment is highly effective in raising audience prosociality and allows DMs to achieve cooperation levels with the out-group that are on par with the in-group, suggesting social image concerns may be strategically leveraged to improve collaborative outcomes across social groups.
GenAI and Social Media Content (with Michael Challis, Mateusz Stalinski, and Adrian Segura)
AI-Assisted Learning (with Yiling Chen, Jeff Jiang, and Gali Noti)
Gradoz, J., & Raux, R. (2021). Trolling in the Deep : Managing Transgressive Content on Online Platforms as a Commons. In Erwin Dekker and Pavel Kuchar (eds), Governing Markets as Knowledge Commons. Cambridge : Cambridge University Press, 217-237.