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Item type:Publication, Access status: without files , Secure human oversight of AI: threat modeling in a socio-technical context(AAAI Press, 2026-10); 9 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , The speed offset problem: A case for principled cultural sensitivity in automated vehicle designAutomated vehicles are widely expected to play a key role in achieving the European Union’s road safety target of ‘Vision Zero’, which aims to eliminate all road fatalities in the European Union by 2050. To achieve this goal, early design strategies for automated vehicles prioritised strict compliance with traffic rules, assuming that rule-abiding behaviour would inherently lead to safer outcomes. Yet rule deviations such as speeding may, counter-intuitively, enhance safety in situations where strict compliance cannot reasonably be expected. Such cases highlight a discrepancy between legal compliance and the informal norms that often govern complex real-world traffic. They also expose an ambivalence in compliance. It is often framed as a necessary precondition for safety, yet in certain contexts, strict compliance can itself generate avoidable risks. This design challenge is what I call the ‘speed offset problem’. The problem becomes particularly evident when examined across different cultural contexts, where expectations around acceptable speeding vary significantly. Building on this, I argue that ‘principled cultural sensitivity’ is fundamental for ethically sound design of automated vehicles. As a normative framework, it demands sensitivity to local driving cultures whilst maintaining firm commitment to core ethical principles. The article develops and applies this framework to the speed offset problem, exemplifying how these guardrails may take shape in concrete cases. Three ethical guardrails restrict the speed offset’s implementation. First, a speed offset is morally permissible only when it increases overall safety. Second, a speed offset must not increase overall safety at the expense of particular groups. Third, a speed offset’s legitimacy must be temporary and revisable. Together, these guardrails ensure that speed offsets are not a concession to unsafe norms but a temporary and ethically guided calibration, bridging current traffic realities with long-term safety goals. The article concludes by outlining implications for engineering practice and regulatory frameworks.Publicationtype: Journal Article15 3 - 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 Article15 8 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Inspirational responsibility, intellectual property, and AI trainingGenerative AI systems, including large language models and image or video generators, are typically trained on data scraped from the internet. Since the creators of the works used for AI training have often neither consented to nor been compensated for such use, this has led to allegations of “theft”. At the same time, since generative AI models do not store copies of their training data, there is a widespread view that they no not necessarily infringe copyright. Moreover, in human creative practice, drawing inspiration from and learning from the works of others is common, if not unavoidable. The central challenge, then, is to formulate an intellectual property right that allows creators to restrict AI training without limiting human learning and creativity. McIntyre ( 2026 ) proposes new strategies for addressing this challenge, most notably through an argument relying on proprietary rights. Critically engaging with this approach, I argue that it overlooks the power dynamics in the art world and leaves creators in a vulnerable and dependent position. Instead, I outline a different pathway to restricting AI training that centres on what I call “inspirational responsibility”. This concept captures the responsibility creators bear for how they use and engage with the work of others. Since human creators can be held inspirationally responsible, whereas AI models cannot, the notion of inspirational responsibility provides a normatively relevant basis for restricting AI training without imposing parallel constraints on human learning and creativity.Publicationtype: Journal Article19 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 Paper24 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Breaking up with normatively monolithic agency with GRACE: a reason-based neuro-symbolic architecture for safe and ethical AI alignment(AAAI Press, 2026-02); ; ; As AI agents become increasingly autonomous, widely deployed in consequential contexts, and efficacious in bringing about real-world impacts, ensuring that their decisions are not only instrumentally effective but also normatively aligned has become critical. We introduce a neuro-symbolic reason-based containment architecture, Governor for Reason-Aligned ContainmEnt (GRACE), that decouples normative reasoning from instrumental decision-making and can contain AI agents of virtually any design. GRACE restructures decision-making into three modules: a Moral Module (MM) that determines permissible macro actions via deontic logic-based reasoning; a Decision-Making Module (DMM) that encapsulates the target agent while selecting instrumentally optimal primitive actions in accordance with derived macro actions; and a Guard that monitors and enforces moral compliance. The MM uses a reason-based formalism providing a semantic foundation for deontic logic, enabling interpretability, contestability, and justifiability. Its symbolic representation enriches the DMM’s informational context and supports formal verification and statistical guarantees of alignment enforced by the Guard. We demonstrate GRACE on an example of a LLM therapy assistant, showing how it enables stakeholders to understand, contest, and refine agent behavior.Publicationtype: Conference Paper13 - 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 Article18 8 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Enter Technico: a technically informed perspective on moral dilemmas in autonomous vehicle policyPublicationtype: Review Article27 13 - 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 Article22 30 - 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