Establish causal mechanisms behind observed shifts in AI acceptance and control preferences

Establish the causal mechanisms that drive the observed changes in public AI acceptance and preferences for human control reported in the two-wave representative Swiss survey, including the decline in acceptance and the increased demand for human oversight.

Background

The study documents significant longitudinal changes—lower AI acceptance and higher preference for human oversight—between early 2022 and mid-2023, coinciding with the generative AI boom. However, the survey design does not identify the causal pathways producing these trends or the demographic disparities that widened over time.

Clarifying causal mechanisms would help policymakers and designers understand the drivers of shifting public attitudes, assess the roles of media exposure, familiarity with AI tools, and contextual differences across scenarios, and better calibrate governance and system design responses.

References

While our study highlights issues related to context-dependent AI acceptance and the amplification of socio-demographic differences, it does not clarify the causal mechanisms behind these trends.

— Reduced AI Acceptance After the Generative AI Boom: Evidence From a Two-Wave Survey Study  (2510.23578 - Baumann et al., 27 Oct 2025) in Section: Limitations and future work (Discussion)

Cross-sectional survey data cannot distinguish whether GenAI use shapes attitudes (a treatment effect) or whether prior attitudes predict adoption (a selection effect), and the specification does not include instrumental variables, panel structure, or other causal identification strategies.

— Who Bears the Risk When Generative AI Enters Transport? A Distributional Sociotechnical Audit of Algorithmic Equity, Synthetic-Data Validity, and Public Trust  (2609.11611 - Rafe et al., 10 Sep 2026) in Section 4.5, subsection “Exploratory Exposure-Attitude Association”