Joachim Wiewiura

Artificial intelligence (AI) is widely discussed as either a threat to democracy or a means of strengthening it. But democracy, an essentially contested concept, invokes many different levels at which AI may assist and obstruct the legitimacy of democratic government. Therefore, the aim of this review is to map how the democratic value of AI, whether positive or negative, depends on different democratic models. The review examines what there is currently no examination of: how democracy is conceptualized, what the cross-cutting patterns are, and how we should prescriptively use democracy in relation to assessing how AI supports or undermines democracy. From this, the review maps three dynamics currently at play in the literature. It shows that conceptual unclarity and pessimistic views on AI’s contribution to democracy often coincide, prescribing the need for conceptual clarity. It shows that democratic models are often “inherited” rather than reworked, prompting the need for greater engagement with how AI reshapes democratic theory. And it maps how democratic assumptions shape evaluations of AI’s democratic implications. From these results, we assess the limits of existing scholarship: without greater conceptual clarity regarding democracy, empirical findings and normative arguments risk talking past one another. Taken together, this review suggests that progress in research on democracy and AI depends not only on further empirical investigation or technical refinement, but also on sustained conceptual work that makes democratic assumptions explicit and thus open to scrutiny.

This article examines AI-based video surveillance systems in public spaces. We investigate a case of AI-based camera technology designed to detect falls into the water and prevent drowning accidents at the harbor front of a large Scandinavian city. This case confronts a common state-citizen tension between safety enhancement and privacy intrusion, and illustrates how involved actors strived to balance this through technological design choices such as thermal imaging. Building on these empirical insights and approaches that seek to deal with the complex safety-privacy tension, we draw out the notion of narrow surveillance as a general approach to safety monitoring in public spaces, aiming to minimize privacy intrusion through three criteria: (i) non-identification, (ii) purpose limitation, and (iii) alignment of interests. By implication, our study contributes new insights to interdisciplinary debates in surveillance studies by providing a framework for evaluating current AI-based surveillance systems and guiding future implementations of such systems.

Artificial intelligence-enabled wearables increasingly generate personalised predictions about future health risks outside traditional clinical settings. While ethical debate has largely focused on issues such as accuracy, privacy, bias and accountability, less attention has been paid to how predictive outputs may reshape the epistemic conditions of the clinical encounter. We introduce the concept of epistemic deadlock to describe situations in which (1) a technologically generated predictive claim is relevant to a patient’s care, but (2) neither patient nor clinician can adequately assess its significance for the individual patient and (3) no proportionate clinical pathway is available to resolve the uncertainty. We argue that this concept identifies a distinctive ethical problem that is narrower than ordinary clinical uncertainty and helps explain why predictive medicine may complicate patient contestability, challenge proportionate clinical judgement and place new pressures on established understandings of clinical authority. Finally, we offer practical recommendations aimed at reducing the conditions under which epistemic deadlock arises through risk contextualisation, clinicians’ competence in communicating and managing predictive uncertainty, protection of clinical discretion and contestability by design. Rather than describing an established feature of contemporary practice, epistemic deadlock is proposed as a concept for analysing an emerging ethical challenge in increasingly predictive forms of healthcare.

This article contends that certain configurations of information networks facilitate specific cognitive states that are instrumental for decision and action on social media. Group-related knowledge and belief states—in particular common knowledge and pluralistic ignorance—may enable strong public signals. Indeed, some network configurations and attitude states foster informational pathologies that may fuel interest bubbles affecting agenda-setting and the generation of narratives in public spheres.

In this small essay, I will reflect on Michael Bérubé and Jennifer Ruth's arguments on the decline of educational professionalism in the United States. The purpose is to consider this loss of professionalism, and I will consider it in light of the arts and humanities in the Danish educational debate. Two reflections are presented: first, the customer relation is reversed in a Scandinavian context where students are politically demanded products rather than informed customers. This implies that universities' output serve political agendas. Second, I suggest that the Scandinavian conception of equality in welfare might entail an inexpedient side-effect when it comes to education. Equality becomes alignment rather than educative edification of individual and, in consequence, communal autonomy. This jeopardises the historically fruitful role of the Scandinavian university – and ultimately caters to a shift from edification to serving interests that are beyond the professional heart of arts and humanities in higher education.