As more decisions are made by automated algorithmic systems, the transparency of these systems has come under scrutiny. While such transparency is typically seen as beneficial, there is a also a critical, Foucauldian account of it. From this perspective, worries have recently been articulated that algorithmic transparency can be used for manipulation, as part of a disciplinary power structure. Klenk (Philosophy & Technology 36, 79, 2023) recently argued that such manipulation should not be understood as exploitation of vulnerable victims, but rather as indifference to whether the information provided enhances decision-making by revealing reasons. This short commentary on Klenk uses Berlin’s (1958) two concepts of liberty to further illuminate the concept of transparency as manipulation, finding alignment between positive liberty and the critical account.

Modern society is increasingly dependent on digital services, making their dependability a top priority. But while there is a consensus that cybersecurity is important, there is no corresponding agreement on the true extent of the problem, the most effective countermeasures, or the proper division of labor and responsibilities. This makes cybersecurity policy very difficult. This article addresses this issue based on observations and experiences from a period of guest research at the Swedish Financial Supervisory Authority (Finansinspektionen), which made it possible to study how cybersecurity policy is developed and implemented in the Swedish financial sector. Observations include policy implementation challenges related to squaring different roles and perspectives mandated by different laws, and to collaboration between independent government authorities, but also policy development challenges: How can the full range of perspectives and tools be included in cybersecurity policy development? As Sweden now revises its cybersecurity policy, this is a key issue.

In recent years, the Swedish public sector has undergone rapid digitalization, while cybersecurity efforts have not kept even steps. This study investigates conditions for cybersecurity work at Swedish administrative authorities by examining organizational conditions at the authorities, what cybersecurity staff do to acquire the cyber situation awareness required for their role, as well as what experience cybersecurity staff have with incidents. In this study, 17 semi-structured interviews were held with respondents from Swedish administrative authorities. The results showed the diverse conditions for cybersecurity work that exist at the authorities and that a variety of roles are involved in that work. It was found that national-level support for cybersecurity was perceived as somewhat lacking. There were also challenges in getting access to information elements required for sufficient cyber situation awareness. 

Modern society makes extensive use of automated algorithmic decisions, fueled by advances in artificial intelligence. However, since these systems are not perfect, questions about fairness are increasingly investigated in the literature. In particular, many authors take a Rawlsian approach to algorithmic fairness. Based on complications with this approach identified in the literature, this article discusses how Rawls’s theory in general, and especially the difference principle, should reasonably be applied to algorithmic fairness decisions. It is observed that proposals to achieve Rawlsian algorithmic fairness often aim to uphold the difference principle in the individual situations where automated decision-making occurs. However, the Rawlsian difference principle applies to society at large and does not aggregate in such a way that upholding it in constituent situations also upholds it in the aggregate. But such aggregation is a hidden premise of many proposals in the literature and its falsity explains many complications encountered. 

Artificial intelligence is making progress, enabling automation of tasks previously the privilege of humans. This brings many benefits but also entails challenges, in particular with respect to ‘black box’ machine learning algorithms. Therefore, questions of transparency and explainability in these systems receive much attention. However, most organizations do not build their software from scratch, but rather procure it from others. Thus, it becomes imperative to consider not only requirements on but also procurement of explainable algorithms and decision support systems. This article offers a first systematic literature review of this area. Following construction of appropriate search queries, 503 unique items from Scopus, ACM Digital Library, and IEEE Xplore were screened for relevance. 37 items remained in the final analysis. An overview and a synthesis of the literature is offered, and it is concluded that more research is needed, in particular on procurement, human-computer interaction aspects, and different purposes of explainability.

With increasing use of artificial intelligence (AI) in high-stakes contexts, a race for “trustworthy AI” is under way. However, Dorsch and Deroy (Philosophy & Technology 37, 62, 2024) recently argued that regardless of its feasibility, morally trustworthy AI is unnecessary: We should merely rely on rather than trust AI, and carefully calibrate our reliance using the reliability scores which are often available. This short commentary on Dorsch and Deroy engages with the claim that morally trustworthy AI is unnecessary and argues that since there are important limits to how good calibration based on reliability scores can be, some residual roles for trustworthy AI (if feasible) are still possible. 

In recent years, the metaverse—some form of immersive digital extension of the physical world—has received much attention. As tech companies present their bold visions, scientists and scholars have also turned to metaverse issues, from technological challenges via societal implications to profound philosophical questions. This article contributes to this growing literature by identifying the possibilities of two dystopian metaverse scenarios, namely one based on the experience machine and one based on demoktesis—two concepts from Nozick (Anarchy, State, and Utopia, Basic Books, 1974). These dystopian scenarios are introduced, and the potential for a metaverse to evolve into either of them is explained. The article is concluded with an argument for why the two dystopian scenarios are not strongly wedded to any particular theory of ethics or political philosophy, but constitute a more general contribution.

With increasing use of automated algorithmic decision-making, issues of algorithmic fairness have attracted much attention lately. In this growing literature, existing concepts from ethics and political philosophy are often applied to new contexts. The reverse—that novel insights from the algorithmic fairness literature are fed back into ethics and political philosophy—is far less established. However, this short commentary on Baumann and Loi (Philosophy & Technology, 36(3), 45 2023) aims to do precisely this. Baumann and Loi argue that among algorithmic group fairness measures proposed, one—sufficiency (well-calibration) is morally defensible for insurers to use, whereas independence (statistical parity or demographic parity) and separation (equalized odds) are not normatively appropriate in the insurance context. Such a result may seem to be of relatively narrow interest to insurers and insurance scholars only. We argue, however, that arguments such as that offered by Baumann and Loi have an important but so far overlooked connection to the derivation of the minimal state offered by Nozick (1974) and thus to political philosophy at large.

We study the impact of data sharing policies on cyber insurance markets. These policies have been proposed to address the scarcity of data about cyber threats, which is essential to manage cyber risks. We propose a Cournot duopoly competition model in which two insurers choose the number of policies they offer (i.e., their production level) and also the resources they invest to ensure the quality of data regarding the cost of claims (i.e., the data quality of their production cost). We find that enacting mandatory data sharing sometimes creates situations in which at most one of the two insurers invests in data quality, whereas both insurers would invest when information sharing is not mandatory. This raises concerns about the merits of making data sharing mandatory.