Public Trust in Automated Decision Systems

This interdisciplinary review examines public trust in automated decision systems. The organizing question is how competence, procedural fairness, transparency, recourse, and institutional legitimacy shape warranted trust. Ten related scholarly sources are synthesized through a decision-centered framework spanning problem definition, mechanism, measurement, evaluation, implementation, and governance. The review does not invent experiments, pooled estimates, or unreported quantitative results. It instead evaluates the strength and transferability of the available evidence, with particular attention to treating acceptance or adoption as equivalent to justified trust. The resulting framework links technical or empirical performance to explicit use conditions and identifies tests that should precede wider adoption in public-sector and socially consequential automation.

1. Araujo, T., Helberger, N., Kruikemeier, S., & Vreese, C. H. D. (2020). In AI we trust? Perceptions about automated decision-making by artificial intelligence. AI & Society, 35(3), 611-623. https://doi.org/10.1007/s00146-019-00931-w DOI
2. Azevedo-Sa, H., Jayaraman, S. K., Esterwood, C. T., Yang, X. J., Robert, L. P., & Tilbury, D. M. (2020). Real-Time Estimation of Drivers’ Trust in Automated Driving Systems. International Journal of Social Robotics, 13(8), 1911-1927. https://doi.org/10.1007/s12369-020-00694-1 DOI
3. Engin, Z., & Treleaven, P. (2018). Algorithmic Government: Automating Public Services and Supporting Civil Servants in using Data Science Technologies. The Computer Journal, 62(3), 448-460. https://doi.org/10.1093/comjnl/bxy082 DOI
4. Kuziemski, M., & Misuraca, G. (2020). AI governance in the public sector: Three tales from the frontiers of automated decision-making in democratic settings. Telecommunications Policy, 44(6), 101976. https://doi.org/10.1016/j.telpol.2020.101976 DOI
5. Kyriakidis, M., Happee, R., & Winter, J. C. F. D. (2015). Public opinion on automated driving: Results of an international questionnaire among 5000 respondents. Transportation Research Part F Traffic Psychology and Behaviour, 32, 127-140. https://doi.org/10.1016/j.trf.2015.04.014 DOI
6. Licht, K. D. F., & Licht, J. D. F. (2020). Artificial intelligence, transparency, and public decision-making. AI & Society, 35(4), 917-926. https://doi.org/10.1007/s00146-020-00960-w DOI
7. Lăzăroiu, G., Neguriţă, O., Grecu, I., Grecu, G., & Mitran, P. C. (2020). Consumers’ Decision-Making Process on Social Commerce Platforms: Online Trust, Perceived Risk, and Purchase Intentions. Frontiers in Psychology, 11, 890. https://doi.org/10.3389/fpsyg.2020.00890 DOI
8. Robinson, S. C. (2020). Trust, transparency, and openness: How inclusion of cultural values shapes Nordic national public policy strategies for artificial intelligence (AI). Technology in Society, 63, 101421. https://doi.org/10.1016/j.techsoc.2020.101421 DOI
9. Ryan, M. (2020). In AI We Trust: Ethics, Artificial Intelligence, and Reliability. Science and Engineering Ethics, 26(5), 2749-2767. https://doi.org/10.1007/s11948-020-00228-y DOI
10. Winfield, A. F. T., & Jirotka, M. (2018). Ethical governance is essential to building trust in robotics and artificial intelligence systems. Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences, 376(2133), 20180085. https://doi.org/10.1098/rsta.2018.0085 DOI