25 results
Now showing1 - 10 of 25
- Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Economic duress as contextual disqualificationPeople often enter contracts under pressure, including economic pressure. Businesses bargain hard, and parties sometimes exploit the fact that others are in financial difficulty. Contract law has long struggled to say when this kind of pressure crosses a line. The problem is how to distinguish legitimate commercial pressure from impermissible economic duress: that is, when pressure renders a contract voidable rather than constituting strategic, even aggressive, bargaining that the law and ethical principles should accept. This article addresses the problem by offering a philosophical analysis of key cases and the widely used three-pronged test. We argue that duress lies not in the presence of psychological pressure or limited alternatives, but in whether the party applying pressure disqualified themselves from claiming contractual rights. This view, which we call Contextual Disqualification, offers a novel and improved account of economic duress that builds upon, but also advances, current legal doctrine. We ground this account in a neglected insight from early modern contract theorist Samuel Pufendorf and extend it by drawing on Stephen Darwall's view on the second-person standpoint.Publicationtype: Journal Article16 9 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Promises and risks in using LLMs in scientific review(IEEE, 2026-03); ; ; ; The current scientific peer review process faces increasing strain and is becoming difficult to sustain. While large language models (LLMs) can help reviewers by summarizing papers, assessing clarity, and generating timely feedback, the research community remains cautious about relying on LLM-based reviews. In this paper, we explore how LLMs could support and potentially improve parts of the peer review process. We examine both the benefits and limitations by analysing LLM-generated reviews of our own previously published papers, comparing them with human reviewer feedback, identifying points of agreement and disagreement, and considering requirements for the responsible and ethical use of LLMs in peer review.Publicationtype: Conference Paper26 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Think first, code later: enhancing student reasoning and learning in practical engineering education through guided generative AI(2026); ; ; 5 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , The ethics of analog AIAI ethics has matured as a field, yet its concepts and tools have been developed almost entirely with digital AI in mind. Emerging “analog” AI approaches, that is, systems that compute over continuous, rather than binary, signals, challenge this digital default. Analog AI promises substantial energy savings and alternative modes of information processing. However, it also departs in ethically significant ways from the widely discussed digital AI systems such that its differences from digital AI could mean that current ethical frameworks may not address analog AI’s unique challenges. This paper highlights how the distinctive characteristics of analog AI raise new ethical questions that require careful consideration. We examine four key areas of AI ethics, namely fairness, privacy, explainability, and safety, and propose a roadmap for the ethical examination of analog AI. Our aim is to lay the foundation for an important but hitherto unrecognized subfield within AI ethics: the ethics of analog AI.Publicationtype: Journal Article19 8 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Rethinking global soil degradation: drivers, impacts, and solutions(American Geophysical Union (AGU), 2025-12); ; ; ; The increasing threat of soil degradation presents significant challenges to soil health, especially within agroecosystems that are vital for food security, climate regulation, and economic stability. This growing concern arises from intricate interactions between land use practices and climatic conditions, which, if not addressed, could jeopardize sustainable development and environmental resilience. This review offers a comprehensive examination of soil degradation, including its definitions, global prevalence, underlying mechanisms, and methods of measurement. It underscores the connections between soil degradation and land use, with a focus on socio‐economic consequences. Current assessment methods frequently depend on insufficient data, concentrate on singular factors, and utilize arbitrary thresholds, potentially resulting in misclassification and misguided decisions. We analyze these shortcomings and investigate emerging methodologies that provide scalable and objective evaluations, offering a more accurate representation of soil vulnerability. Additionally, the review assesses both physical and biological indicators, as well as the potential of technologies such as remote sensing, artificial intelligence, and big data analytics for enhanced monitoring and forecasting. Key factors driving soil degradation, including unsustainable agricultural practices, deforestation, industrial activities, and extreme climate events, are thoroughly examined. The review emphasizes the importance of healthy soils in achieving the United Nations Sustainable Development Goals, particularly concerning food and water security, ecosystem health, poverty alleviation, and climate action. It suggests future research directions that prioritize standardized metrics, interdisciplinary collaboration, and predictive modeling to facilitate more integrated and effective management of soil degradation in the context of global environmental changes.Publicationtype: Review Article23 34 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Matt King, Simply Responsible: Basic Blame, Scant Praise, and Minimal AgencyPublicationtype: Journal Article11 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , AI and responsibility : no gap, but abundanceThe best-performing AI systems, such as deep neural networks, tend to be the ones that are most difficult to control and understand. For this reason, scholars worry that the use of AI would lead to so-called responsibility gaps, that is, situations in which no one is morally responsible for the harm caused by AI, because no one satisfies the so-called control condition and epistemic condition of moral responsibility. In this article, I acknowledge that there is a significant challenge around responsibility and AI. Yet I don't think that this challenge is best captured in terms of a responsibility gap. Instead, I argue for the opposite view, namely that there is responsibility abundance, that is, a situation in which numerous agents are responsible for the harm caused by AI, and that the challenge comes from the difficulties of dealing with such abundance in practice. I conclude by arguing that reframing the challenge in this way offers distinct dialectic and theoretical advantages, promising to help overcome some obstacles in the current debate surrounding ‘responsibility gaps’.Publicationtype: Journal Article51 64 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Moral responsibility and artificial intelligenceThis book introduces a novel puzzle, the 'Lorry Driver Paradox', to advance our understanding of moral responsibility beyond current paradigms, to connect moral philosophy and legal scholarship in new ways, and to break new ground in the ethics of AI. Part 1 introduces the Lorry Driver Paradox as a set of three individually plausible but jointly inconsistent claims. It then develops and defends the concept of strict moral answerability as the most effective solution, making it the central idea of the book, alongside an account of how ‘taking responsibility’ could amount to a new, hitherto neglected normative power and an exploration of the significance of apologies in our social practices. Part 2 extends this discussion to the context of artificial intelligence, proposing a major shift in how we currently think about responsibility and AI. It challenges the conventional notion of AI-generated ‘responsibility gaps’ and, instead, proposes the idea of ‘responsibility abundance’. This reframing, it is argued, offers distinct theoretical, dialectical, and practical advantages. Significant parts of these arguments draw on legal scholarship, particularly considerations about reverse burdens of proof in criminal law and the waiving of state immunity in public international law. On these grounds, the book also pursues the methodological idea of a ‘legal lead’, that is, the idea that we can advance our understanding of moral responsibility by investigating (selected aspects of) legal responsibility, and not just the other way around.Publicationtype: Book39 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , 14 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Rethinking trust in responsible AI(CEUR-WS, 2025); ; ; ; Trust is widely recognized as a core principle of Responsible AI, yet its interpretation varies significantly across disciplines. This paper examines how computer science, sociology, philosophy, and law conceptualize trust in AI systems, highlighting both tensions and complementarities. From a computer science perspective, trust is often approached as a set of system-level properties that should be formalized and evaluated with metrics. In contrast, the social sciences and humanities emphasize its relational, normative, and institutional dimensions. We argue that trust cannot be reduced to a single system property or technical measure, as it emerges from social-technical interactions involving users, developers, legal norms, and social expectations. To support interdisciplinary dialogue, we propose treating trust as a boundary concept that enables cooperation across epistemic communities acknowledging conceptual differences. Trust is widely recognized as a core principle of Responsible AI, yet its interpretation varies significantly across disciplines. This paper examines how computer science, sociology, philosophy, and law conceptualize trust in AI systems, highlighting both tensions and complementarities. From a computer science perspective, trust is often approached as a set of system-level properties that should be formalized and evaluated with metrics. In contrast, the social sciences and humanities emphasize its relational, normative, and institutional dimensions. We argue that trust cannot be reduced to a single system property or technical measure, as it emerges from social-technical interactions involving users, developers, legal norms, and social expectations. To support interdisciplinary dialogue, we propose treating trust as a boundary concept that enables cooperation across epistemic communities acknowledging conceptual differences.Publicationtype: Conference Paper22 4