arXiv subjects

Mengchen Dong

Publications and source records attributed to Mengchen Dong.

4 recordsLinked to original sources

arXiv2026

Human diversity fuels collective creativity that large language models cannot simulate or sustain

Diverse human groups produce diverse ideas, the raw material of innovation. Generative AI challenges this engine twice over: everyday AI assistance may homogenize what diverse people create, and AI-simulated diversity may replace the people altogether. We tested both challenges in a preregistered creative metaphor experiment with native (L1) and non-native (L2) English writers, who wrote without AI, with AI-generated ideas (AI ideation), or with AI refining their own ideas (AI refinement). L2 writers contributed more collective diversity than L1 writers, with native-language ideation showing the most diverse pools. AI ideation compressed collective diversity for everyone and left the L2 advantage undetectable, whereas AI refinement preserved both. We then simulated the entire writer pool using personas built from participants' real backgrounds, three model families, native-language prompting, and elevated sampling temperatures. Every simulated pool fell below every human pool, and pushing models further induced diversity only through degenerate text. However, at the individual level, AI ideation raised writers' ratings, pitting private incentives against the collective good, except when L2 writers used their native language, which benefited both. Human diversity remains a valuable creative resource that current AI cannot simulate or sustain; the design of human-AI collaborative workflows determines whether it survives.

cs.HC↗

arXiv2025

Experimental Evidence That AI-Managed Workers Tolerate Lower Pay Without Demotivation

Experimental evidence on worker responses to AI management remains mixed, partly due to limitations in experimental fidelity. We address these limitations with a customized workplace in the Minecraft platform, enabling high-resolution behavioral tracking of autonomous task execution, and ensuring that participants approach the task with well-formed expectations about their own competence. Workers (N = 382) completed repeated production tasks under either human, AI, or hybrid management. An AI manager trained on human-defined evaluation principles systematically assigned lower performance ratings and reduced wages by 40\%, without adverse effects on worker motivation and sense of fairness. These effects were driven by a muted emotional response to AI evaluation, compared to evaluation by a human. The very features that make AI appear impartial may also facilitate silent exploitation, by suppressing the social reactions that normally constrain extractive practices in human-managed work.

cs.CY↗

arXiv2024

False consensus biases AI against vulnerable stakeholders

The deployment of AI systems for welfare benefit allocation allows for accelerated decision-making and faster provision of critical help, but has already led to an increase in unfair benefit denials and false fraud accusations. Collecting data in the US and the UK (N = 2449), we explore the public acceptability of such speed-accuracy trade-offs in populations of claimants and non-claimants. We observe a general willingness to trade off speed gains for modest accuracy losses, but this aggregate view masks notable divergences between claimants and non-claimants. Although welfare claimants comprise a relatively small proportion of the general population (e.g., 20% in the US representative sample), this vulnerable group is much less willing to accept AI deployed in welfare systems, raising concerns that solely using aggregate data for calibration could lead to policies misaligned with stakeholder preferences. Our study further uncovers asymmetric insights between claimants and non-claimants. The latter consistently overestimate claimant willingness to accept speed-accuracy trade-offs, even when financially incentivized for accurate perspective-taking. This suggests that policy decisions influenced by the dominant voice of non-claimants, however well-intentioned, may neglect the actual preferences of those directly affected by welfare AI systems. Our findings underline the need for stakeholder engagement and transparent communication in the design and deployment of these systems, particularly in contexts marked by power imbalances.

cs.CY↗

arXiv2023

Fears about AI-mediated communication are grounded in different expectations for one's own versus others' use

The rapid development of AI-mediated communication technologies (AICTs), which are digital tools that use AI to augment interpersonal messages, has raised concerns about the future of interpersonal trust and prompted discussions about disclosure and uptake. This paper contributes to this discussion by assessing perceptions about the acceptability and use of open and secret AICTs for oneself and others. In two studies with representative samples (UK: N=477, US: N=765), we found that secret AICT use is deemed less acceptable than open AICT use, people tend to overestimate others' AICT use, and people expect others to use AICTs irresponsibly. Thus, we raise concerns about the potential for misperceptions and different expectations for others to drive self-fulfilling pessimistic outlooks about AI-mediated communication.

cs.HC↗