by Shaul A. Duke, Peter Sandøe, Thomas Andras Matthiessen Skelly, and Sune Holm
Duke, S. A., Sandøe, P., Skelly, T. A. M., & Holm, S. (2025). Supportive but apprehensive: Ambivalent attitudes towards a Danish public health AI and surveillance-driven project. Big Data & Society, 12(4), https://doi.org/10.1177/20539517251386035. (Original work published 2025)
Healthcare big data projects are prone to give rise to ambivalent attitudes in the general public because they both tend to include features that seem promising and have the potential of benefiting the public, while also having features that seem risky, with potentially harmful implications on individuals and society. For instance, the goal of such projects most often promises to improve public health and address a real problem that exists and negatively affects people’s lives. However, the method of achieving this goal most often entails the expansion of surveillance measures, which may challenge individuals’ privacy and autonomy, including the use of artificial intelligence methods with all the ethical challenges they introduce, and the involvement of partners that members of the public might disapprove of.
Similar to other social situations – such as when having to cast a vote for a candidate or a political party – it is impossible for members of the public just to support the features they like and discard the rest. Instead, one needs to consider the package as a whole. Thus, it is not possible to vote for half a party, or for just part of the political agenda of a candidate. The literature on the political arena has taught us that in such mixed-bag situations, voters often become ambivalent, with a tension between support and apprehension building up. Our research, based on long semi-structured interviews, has similarly found that ambivalence towards healthcare big data projects manifests in a dual stance.
Most interestingly, we found that interviewees are using several techniques to alleviate the ambivalent tension once we presented the project and asked for their opinion about it. Specifically, these techniques enabled them to support the project as a whole, while still being apprehensive about some aspects within it. This reaction we understand as driven by their reluctance to miss out on potential healthcare benefits.
While duality was widely recognized by several texts that examine public opinion on healthcare big data/AI projects, ambivalence was not, and constitutes this article’s contribution to this field. We hope that this article will inspire other scholars to consider ambivalence and its alleviation in future research.
