In recent years, artificial intelligence (AI) has made great progress. However, despite impressive results, modern data-driven AI systems are often very difficult to understand, challenging their use in software business and prompting the emergence of the explainable AI (XAI) field. This paper explores how machine learning (ML) students explain their models and draws implications for practice from this. Data was collected from ML master students, who were given a two-part assignment. First they developed a model predicting insurance claims based on an existing data set, then they received a request for explanation of insurance premiums in accordance with the GDPR right to meaningful information and had to come up with such an explanation. The students also peer-graded each other’s explanations. Analyzing this data set and comparing it to responses from actual insurance firms from a previous study illustrates some potential pitfalls—narrow technical focus and offering mere data dumps. There were also some promising directions—feature importance, graphics, and what-if scenarios—where the software business practice could benefit from being inspired by the students. The paper is concluded with a reflection about the importance of multiple kinds of expertise and team efforts for making the most of XAI in practice.

Increasing use of new digital services offers tremendous opportunities for modern society, but also entails new risks. One tool for managing cyber risk is cyber insurance. While cyber insurance has attracted much attention and optimism, interdependent cyber risks and lack of actuarial data have prompted some insurers to adopt a more proactive role, not only insuring losses but also assisting clients with preventive work such as managed detection and response solutions, i.e., investments in their own cybersecurity. The purpose of this paper is to propose and theoretically investigate yet a further extension of this role, where insurers facilitate security investments between interdependent firms, which get the opportunity to invest a share of their insurance premiums to improve the security of each other. It is demonstrated that if insurers can facilitate such investments, then under common theoretical assumptions this can make a positive contribution to overall welfare. The paper is concluded by a discussion of the relevance and applicability of this theoretical contribution in practice. 

This paper presents an in-depth examination of the use of simulations in economic cybersecurity decision-making, highlighting the dual nature of their potential and the challenges they present. Drawing on examples from existing studies, we explore the role of simulations in generating new knowledge about probabilities and consequences in the cybersecurity domain, which is essential in understanding and managing risk and uncertainty. Additionally, we introduce the concepts of "bookkeeping"and "abstraction"within the context of simulations, discussing how they can sometimes fail and exploring the underlying reasons for their failures. This discussion leads us to suggest a framework of considerations for effectively utilizing simulations in cybersecurity. This framework is designed not as a rigid checklist but as a guide for critical thinking and evaluation, aiding users in assessing the suitability and reliability of a simulation model for a particular decision-making context. Future work should focus on applying this framework in real-world settings, continuously refining the use of simulations to ensure they remain effective and relevant in the dynamic field of cybersecurity. 

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.