This paper investigates the application of artificial intelligence (AI) language models in writing policy briefing notes within the context of public administration by juxtaposing the technologies’ performance against the traditional reliance on human expertise. Briefing notes are pivotal in informing decision-making processes in government contexts, which generally require high accuracy, clarity, and issue-relevance. Given the increasing integration of AI across various sectors, this study aims to evaluate the effectiveness and acceptability of AI-generated policy briefing notes. Using a structured evaluation-by-experts methodology, (...) the research scrutinizes and compares the output of three leading AI language models—OpenAI’s ChatGPT, Google’s Gemini, and Mistral’s Le Chat—across ten critical dimensions reflective of policy briefing notes quality. These dimensions encompass both structural and content-related aspects, ranging from linguistic precision to the depth and applicability of the generated information. The discussion is anchored in the technology acceptance model (TAM) theory and its extensions, which offer a theoretical framework for understanding the factors influencing the adoption and usefulness of technology in public administration. Our comparative analysis reveals that while AI models exhibit notable competencies in meeting some structural and linguistic benchmarks, they fail to address the nuances and depth required by policy experts for undertaking informed decision-making adequately. This discrepancy underscores the enduring value of human expertise in synthesizing complex information and navigating ethical considerations, even as AI enhances efficiency in certain aspects of the policy briefing note crafting process. (shrink)

This article draws on contributions from the Sociology of Science and Technology and Science and Technology Studies, the Sociology of Risk and Uncertainty, and the Sociology of Work, focusing on the transformations of employment regarding expanded automation, robotization and informatization. The new work patterns emerging due to the introduction of software and hardware technologies, which are based on artificial intelligence, algorithms, big data gathering and robotic systems are examined closely. This article attempts to “open the black boxes” of the “black (...) carpet” (robotic sorting system) and examine the reorganization of Greek postal services through the introduction of software and hardware technologies, highlighting a) the high risk of flexible, pluralistic, decentralized (under)employment and aspects of the sub-politics of automation, b) the new forms of work organization and c) aspects of labor precariousness as a result of de-specialization or “flexible expertise” interconnected with new forms of inequalities based on AI, algorithms, big data and robots as reflected in the lived experiences of workers. The above are investigated through primary empirical social research in postal service companies in Greece. (shrink)

The complexity and emergent autonomy of Generative AI systems introduce challenges in predictability and legal compliance. This paper analyses some of the legal and regulatory implications of such challenges in the European Union context, focusing on four areas: liability, privacy, intellectual property, and cybersecurity. It examines the adequacy of the existing and proposed EU legislation, including the Artificial Intelligence Act (AIA), in addressing the challenges posed by Generative AI in general and LLMs in particular. The paper identifies potential gaps and (...) shortcomings in the EU legislative framework and proposes recommendations to ensure the safe and compliant deployment of generative models. (shrink)

The rapid diffusion of artificial intelligence (AI) technologies in the defence domain raises challenges for the ethical governance of these systems. A recent shift from the what to the how of AI ethics sees a nascent body of literature published by defence organisations focussed on guidance to implement AI ethics principles. These efforts have neglected a crucial intermediate step between principles and guidance concerning the elicitation of ethical requirements for specifying the guidance. In this article, we outline the key normative (...) choices and corresponding tradeoffs that are involved in specifying guidance for the implementation of AI ethics principles in the defence domain. These correspond to: the AI lifecycle model used; the scope of stakeholder involvement; the accountability goals chosen; the choice of auditing requirements; and the choice of mechanisms for transparency and traceability. We provide initial recommendations for navigating these tradeoffs and highlight the importance of a pro-ethical institutional culture. (shrink)

Machine unlearning (MU) is often analyzed in terms of how it can facilitate the “right to be forgotten.” In this commentary, we show that MU can support the OECD’s five principles for trustworthy AI, which are influencing AI development and regulation worldwide. This makes it a promising tool to translate AI principles into practice. We also argue that the implementation of MU is not without ethical risks. To address these concerns and amplify the positive impact of MU, we offer policy (...) recommendations across six categories to encourage the research and uptake of this potentially highly influential new technology. (shrink)

This article argues that due to the difficulty in governing AI, it is essential to develop measures implemented early in the AI research process. The goal of dual use considerations is to create robust strategies that uphold AI’s integrity while protecting societal interests. The challenges of applying dual use frameworks to AI research are examined and dual use and dual use research of concern (DURC) are defined while highlighting the difficulties in balancing the technology’s benefits and risks. AI’s dual use (...) potential is discussed, particularly in areas like NLP and LLMs, and the need for early consideration of dual use risks to ensure ethical and secure development is underscored. In the section on shared responsibilities in AI research and avenues for mitigation strategies the importance of early-stage risk assessments and ethical guidelines to mitigate misuse is emphasized, accentuating self-governance within scientific communities and structured measures like checklists and pre-registration to promote responsible research practices. The final section argues that research ethics committees play a crucial role in evaluating the dual use implications of AI technologies within the research pipeline. The need for tailored ethics review processes is articulated, drawing parallels with medical research ethics committees. (shrink)

Artificial intelligence (AI) is being tightly integrated into healthcare today. Even though AI is being utilized in healthcare, its application in clinical settings and health professions education is still controversial. The study described the perceptions of AI and its integration into health professions education and healthcare among health professions students. This descriptive phenomenological study analyzed the data from a purposive sample of 33 health professions students at a university in Kazakhstan using the thematic approach. Data collection was conducted from March (...) 30 to May 5, 2023, using an online questionnaire with four open-ended questions. Four significant themes describing the perceptions of the health professions students on AI and its integration into health profession education and healthcare was derived: enhanced interactive learning experiences, integrating artificial intelligence-powered diagnostic and decision-support tools into health professions education, AI technologies assist students in preparing for future roles in healthcare and integrating AI technologies presents several challenges. Health professions students perceived that integrating AI into health professions education and healthcare enhances their interactive learning experiences, increases knowledge and application of complicated medical topics, and prepares them for future healthcare roles. (shrink)

Recital 33 GDPR has often been interpreted as referring to ‘broad consent’. This version of informed consent was intended to allow data subjects to provide their consent for certain areas of research, or parts of research projects, conditional to the research being in line with ‘recognised ethical standards’. In this article, we argue that broad consent is applicable in the emerging field of Computational Social Science (CSS), which lies at the intersection of data science and social science. However, the lack (...) of recognised ethical standards specific to CSS poses a practical barrier to the use of broad consent in this field and other fields that lack recognised ethical standards. Upon examining existing research ethics standards in social science and data science, we argue that they are insufficient for CSS. We further contend that the fragmentation of European Union (EU) law and research ethics sources makes it challenging to establish universally recognised ethical standards for scientific research. As a result, CSS researchers and other researchers in emerging fields that lack recognised ethical standards are left without sufficient guidance on the use of broad consent as provided for in the GDPR. We conclude that responsible EU bodies should provide additional guidance to facilitate the use of broad consent in CSS research. (shrink)

Artificial intelligence (AI) has long been recognised as a challenge to responsibility. Much of this discourse has been framed around robots, such as autonomous weapons or self-driving cars, where we arguably lack control over a machine’s behaviour and therefore struggle to identify an agent that can be held accountable. However, most of today’s AI is based on machine-learning technology that does not act on its own, but rather serves as a decision-support tool, automatically analysing data to help human agents make (...) better decisions. I argue that decision-support tools pose a challenge to responsibility that goes beyond the familiar problem of finding someone to blame or punish for the behaviour of agent-like systems. Namely, they pose a problem for what we might call “decision ownership”: they make it difficult to identify human agents to whom we can attribute value-judgements that are reflected in decisions. Drawing on recent philosophical literature on responsibility and its various facets, I argue that this is primarily a problem of attributability rather than of accountability. This particular responsibility problem comes in different forms and degrees, most obviously when an AI provides direct recommendations for actions, but also, less obviously, when it provides mere descriptive information on the basis of which a decision is made. (shrink)

This paper puts forward a wish list of requirements for formal fairness in the specific context of triage in emergency departments (EDs) and maps the empirical and conceptual research questions that need to be addressed in this context in the near future. The pandemic has brought to the fore the necessity for public debate about how to allocate resources fairly in a situation of great shortage. However, issues of fairness arise also outside of pandemics: decisions about how to allocate resources (...) are structurally unavoidable in healthcare systems, as value judgements underlie every allocative decision, although they are not always easily identifiable. In this paper, we set out to bridge this gap in the context of EDs. In the first part, we propose five formal requirements specifically applied for ED triage to be considered fair and legitimate: publicity, accessibility, relevance, standardisability and accountability. In the second part of the paper, we map the conceptual and empirical ethics questions that will need to be investigated to assess whether healthcare systems guarantee a formally just ED triage. In conclusion, we argue that there is a vast research landscape in need of an in-depth conceptual and empirical investigation in the context of ED triage in ordinary times. Addressing both types of questions in this context is vital for promoting a fair and legitimate ED triage and for fostering reflection on formal fairness allocative issues beyond triage. (shrink)