The paper problematises the reliability and ethics of using social media data, such as sourced from Twitter or Instagram, to carry out health-related research. As in many other domains, the opportunity to mine social media for information has been hailed as transformative for research on well-being and disease. Considerations around the fairness, responsibilities and accountabilities relating to using such data have often been set aside, on the understanding that as long as data were anonymised, no real ethical or scientific issue (...) would arise. We first counter this perception by emphasising that the use of social media data in health research can yield problematic and unethical results. We then provide a conceptualisation of methodological data fairness that can complement data management principles such as FAIR by enhancing the actionability of social media data for future research. We highlight the forms that methodological data fairness can take at different stages of the research process and identify practical steps through which researchers can ensure that their practices and outcomes are scientifically sound as well as fair to society at large. We conclude that making research data fair as well as FAIR is inextricably linked to concerns around the adequacy of data practices. The failure to act on those concerns raises serious ethical, methodological and epistemic issues with the knowledge and evidence that are being produced. (shrink)

This study explores the phenomenon of artificial intelligence (AI) aversion within the context of public policy, building on prior research on algorithmic aversion. I aim to establish a clear conceptual distinction between algorithms and AI in the public’s perception and develop a robust metric for assessing AI aversion. Utilizing a national survey, I employed affective imagery testing to compare Americans emotional responses towards AI, algorithms, and advanced technology. The findings reveal that AI elicits significantly more negative emotional responses than the (...) other two, indicating its unique position in public perception. I then construct the Artificial Intelligence Aversion Index (AIAI) based on responses to policy-related vignettes. Regression analyses showed a strong negative relationship between the AIAI and public support for both current and future AI applications within public policy, with aversion more pronounced towards potential future uses. These insights underscore the importance of understanding public sentiment towards AI to inform policymaking as well as helping to establish a framework by which to evaluate aversion levels. (shrink)

Our article examines how journalistic reports and online comments have made sense of computational politics. It treats the discourse around data-driven campaigns as its object of analysis and codifies four main perspectives that have structured the debates about the use of large data sets and data analytics in elections. We study American, British, and German sources on the 2016 United States presidential election, the 2017 United Kingdom general election, and the 2017 German federal election. There, groups of speakers maneuvered between (...) enthusiastic, skeptical, agnostic, or admonitory stances and so cannot be clearly mapped onto these four discursive positions. Coming along with the inconsistent accounts, public sensemaking was marked by an atmosphere of speculation about the substance and effects of computational politics. We conclude that this equivocality helped journalists and commentators to sideline prior reporting on the issue in order to repeatedly rediscover the practices they had already covered. (shrink)