Social media platforms have been rapidly increasing the number of informational labels they are appending to user-generated content in order to indicate the disputed nature of messages or to provide context. The rise of this practice constitutes an important new chapter in social media governance, as companies are often choosing this new “middle way” between a laissez-faire approach and more drastic remedies such as removing or downranking content. Yet information labeling as a practice has, thus far, been mostly tactical, reactive, (...) and without strategic underpinnings. In this paper, we argue against defining success as merely the curbing of misinformation spread. The key to thinking about labeling strategically is to consider it from an epistemic perspective and to take as a starting point the “social” dimension of online social networks. The strategy we articulate emphasizes how the moderation system needs to improve the epistemic position and relationships of platform users — i.e., their ability to make good judgements about the sources and quality of the information with which they interact on the platform — while also appropriately respecting sources, seekers, and subjects of information. A systematic and normatively grounded approach can improve content moderation efforts by providing clearer accounts of what the goals are, how success should be defined and measured, and where ethical considerations should be taken into consideration. We consider implications for the policies of social media companies, propose new potential metrics for success, and review research and innovation agendas in this regard. (shrink)

Over recent years, the stakes and complexity of online content moderation have been steadily raised, swelling from concerns about personal conflict in smaller communities to worries about effects on public life and democracy. Because of the massive growth in online expressions, automated tools based on machine learning are increasingly used to moderate speech. While ‘design-based governance’ through complex algorithmic techniques has come under intense scrutiny, critical research covering algorithmic content moderation is still rare. To add to our understanding of concrete (...) instances of machine moderation, this article examines Perspective API, a system for the automated detection of ‘toxicity’ developed and run by the Google unit Jigsaw that can be used by websites to help moderate their forums and comment sections. The article proceeds in four steps. First, we present our methodological strategy and the empirical materials we were able to draw on, including interviews, documentation, and GitHub repositories. We then summarize our findings along five axes to identify the various threads Perspective API brings together to deliver a working product. The third section discusses two conflicting organizational logics within the project, paying attention to both critique and what can be learned from the specific case at hand. We conclude by arguing that the opposition between ‘human’ and ‘machine’ in speech moderation obscures the many ways these two come together in concrete systems, and suggest that the way forward requires proactive engagement with the design of technologies as well as the institutions they are embedded in. (shrink)

Artificial Intelligence-as-a-Service empowers individuals and organisations to access AI on-demand, in either tailored or ‘off-the-shelf’ forms. However, institutional separation between development, training and deployment can lead to critical opacities, such as obscuring the level of human effort necessary to produce and train AI services. Information about how, where, and for whom AI services have been produced are valuable secrets, which vendors strategically disclose to clients depending on commercial interests. This article provides a critical analysis of how AIaaS vendors manipulate the (...) visibility of human labour in AI production based on whether the vendor relies on paid or unpaid labour to fill interstitial gaps. Where vendors are able to occlude human labour in the organisational ‘backstage,’ such as in data preparation, validation or impersonation, they do so regularly, further contributing to ongoing techno-utopian narratives of AI hype. Yet, when vendors must co-produce the AI service with the client, such as through localised AI training, they must ‘lift the curtain’, resulting in a paradoxical situation of needing to both perpetuate dominant AI hype narratives while emphasising AI’s mundane limitations. (shrink)

This paper has 3 main goals: (1) to clarify the role of Artificial Intelligence (AI)—along with algorithms more broadly—in online radicalization that results in ‘real world violence’; (2) to argue that technological solutions (like better AI) are inadequate proposals for this problem given both technical and social reasons; and (3) to demonstrate that platform companies’ (e.g., Meta, Google) statements of preference for technological solutions functions as a type of propaganda that serves to erase the work of the thousands of human (...) content moderators and conceal the harms they experience. I argue that the proper assessment of these important, related issues must be free of the obfuscation that the ‘better AI’ proposal generates. For this reason, I describe the AI-centric solutions favoured by major platform companies as a type of obfuscating and dehumanizing propaganda. (shrink)

Recent buzzes around big data, data science and artificial intelligence portray a data-driven future for healthcare. As a response, Europe's key players have stimulated the use of big data technologies to make healthcare more efficient and effective. Critical Data Studies and Science and Technology Studies have developed many concepts to reflect on such overly positive narratives and conduct critical policy evaluations. In this study, we argue that there is also much to be learned from studying how professionals in the healthcare (...) field affectively engage with this strong European narrative in concrete big data projects. We followed twelve hospital-based big data pilots in eight European countries and interviewed 145 professionals between 2018 and 2020. In this study, we introduce the metaphor of dreams to describe how professionals link the big data promises to their own frustrations, ideas, values and experiences with healthcare. Our research answers the question: how do professionals in concrete data-driven initiatives affectively engage with European Union's data hopes in their ‘dreams’ – and with what consequences? We describe the dreams of being seen, of timeliness, of connectedness and of being in control. Each of these dreams emphasizes certain aspects of the grand narrative of big data in Europe, makes particular assumptions and has different consequences. We argue that including attention to these dreams in our work could help shine an additional critical light on the big data developments and stimulate the development of responsible data-driven healthcare. (shrink)