Search this site Embedded Files Skip to main content Skip to navigation raphaelraux Home Research raphaelraux Home Research More Home Research Research Working Papers 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: Marginal Revolution Harvard Griffin GSAS news Harvard Horizons "Ted Talk style" presentation [NEW!] Strategic Slanting ( new version of " Signaling Universalism " ; 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. Work in Progress GenAI and Social Media Content (with Michael Challis, Mateusz Stalinski, and Adrian Segura) AI-Assisted Learning (with Yiling Chen, Jeff Jiang, and Gali Noti) Book Chapter 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. Google Sites Report abuse Page details Page updated Google Sites Report abuse