This talk examines how corpus linguistics and artificial intelligence treasure the potential to reshape contemporary language learning ecologies. It argues that the rapid normalisation of generative AI has intensified the need for pedagogical models that combine low-friction access to language support with transparent methods grounded in attested usage. Drawing on ecological perspectives and recent empirical research, the talk shows how AI-driven environments expand opportunities for language learning while creating risks related to opacity and over-reliance. Corpus linguistics, data-driven learning and corpus literacy offer a complementary foundation by providing traceable evidence, reproducible analyses, and practices that foster learners’ critical judgement. Two convergence scenarios are proposed: AI as an extension of DDL, and corpus literacy as the operational core of critical AI literacy. Together, these scenarios illustrate how open-box pedagogies can reconcile responsiveness and accountability, ensuring that AI-mediated learning remains anchored in transparent processes and empirically grounded language knowledge.

This article critically examines the integration of artificial intelligence (AI) into nuclear decision-making processes and its implications for deterrence strategies in the Third Nuclear Age. While realist deterrence logic assumes that the threat of mutual destruction compels rational actors to act cautiously, AI disrupts this by adding speed, opacity and algorithmic biases to decision-making processes. The article focuses on the case of Russia to explore how different understandings of deterrence among nuclear powers could increase the risk of misperceptions and inadvertent escalation in an AI-influenced strategic environment. I argue that AI does not operate in a conceptual vacuum: the effects of its integration depend on the strategic assumptions guiding its use. As such, divergent interpretations of deterrence may render AI-supported decision making more unpredictable, particularly in high-stakes nuclear contexts. I also consider how these risks intersect with broader arms race dynamics. Specifically, the pursuit of AI-enabled capabilities by global powers is not only accelerating military modernisation but also intensifying the security dilemma, as each side fears falling behind. In light of these challenges, this article calls for greater attention to conceptual divergence in deterrence thinking, alongside transparency protocols and confidence-building measures aimed at mitigating misunderstandings and promoting stability in an increasingly automated military landscape.

This article investigates the profound impact of artificial intelligence (AI) and big data on political and military deliberations concerning the decision to wage war. By conceptualising AI as part of a broader, interconnected technology ecosystem – encompassing data, connectivity, energy, compute capacity and workforce – the article introduces the notion of “architectures of AI” to describe the underlying infrastructure shaping contemporary security and sovereignty. It demonstrates how these architectures concentrate power within a select number of technology companies, which increasingly function as national security actors capable of influencing state decisions on the resort to force. The article identifies three critical factors that collectively alter the calculus of war: (i) the concentration of power across the architectures of AI, (ii) the diffusion of national security decision making, and (iii) the role of AI in shaping public opinion. It argues that, as technology companies amass unprecedented control over digital infrastructure and information flows, most nation states – particularly smaller or less technologically advanced ones – experience diminished autonomy in decisions to use force. The article specifically examines how technology companies can coerce, influence or incentivise the resort-to-force decision making of smaller states, thereby challenging traditional notions of state sovereignty and international security.

The Council of Europe has very recently adopted the Framework Convention on Artificial Intelligence and Human Rights, Democracy and the Rule of Law. This article provides an initial analysis of the CoE AI Convention. It emphasises the necessity of understanding the CoE AI Convention within the context of its adoption as an international treaty negotiated within the Council of Europe. This context has affected its scope in terms of how the treaty includes the regulation of the usage of AI systems by both public authorities and private actors. The detailed review of the available negotiation documents reveals that the concrete level of protection offered by the Convention has been lowered. This includes the risk-based approach, which shapes the obligations undertaken by States under the treaty. This approach is explained and contrasted with the approach under the EU AI Act. The argument that emerges is that the absence of categorisation of risk levels in the treaty is related to its higher level of abstraction, which does not necessarily imply less robust obligations. The content of these obligations is also clarified in light of the requirement imposed by the treaty of consistency with human rights law. An argument is advanced that the principles formulated in the treaty – human dignity and individual autonomy, transparency and oversight, accountability, non-discrimination, data protection, reliability, risk-management – can offer interpretative guidance for the development of human rights standards.

Interest in the use of chatbots powered by large language models (LLMs) to support women and girls in conflicts and humanitarian crises, including survivors of gender-based violence (GBV), appears to be increasing. Chatbots could offer a last-resort solution for GBV survivors who are unable or unwilling to access relevant information and support in a safe and timely manner. With the right investment and guard-rails, chatbots might also help treat some symptoms related to mental health and psychosocial conditions, extending mental health and psychosocial support (MHPSS) to crisis-affected communities. However, the use of chatbots can also increase risks for individual users – for example, generating unintended harms when a chatbot hallucinates or produces errors. In this paper, we critically examine the opportunities and limitations of using LLM-powered chatbots1 that provide direct care and support to women and girls in conflicts and humanitarian crises, with a specific focus on GBV survivors. We find some evidence in the global North to suggest that the use of chatbots may reduce self-reported feelings of loneliness for some individuals, but we find less evidence on the role and effectiveness of chatbots in crisis counselling and treating depression, post-traumatic or somatic symptomology, particularly as it relates to GBV in emergencies or other traumatic events that occur in armed conflicts and humanitarian crises. Drawing on key expert interviews as well as evidence and research from adjacent scholarship – such as feminist AI, trauma treatment, GBV, and MHPSS in conflicts and emergencies – we conclude that the potential benefits of GBV-related, AI-enabled talk therapy chatbots do not yet outweigh their risks, particularly when deployed in high-stakes scenarios and contexts such as armed conflicts and humanitarian crises.

Two modern trends in insurance are data-intensive underwriting and behaviour-based insurance. Data-intensive underwriting means that insurers analyse more data for estimating the claim cost of a consumer and for determining the premium based on that estimation. Insurers also offer behaviour-based insurance. For example, some car insurers use artificial intelligence (AI) to follow the driving behaviour of an individual consumer in real time and decide whether to offer that consumer a discount. In this paper, we report on a survey of the Dutch population (N = 999) in which we asked people’s opinions about examples of data-intensive underwriting and behaviour-based insurance. The main results include: (i) If survey respondents find an insurance practice unfair, they also find the practice unacceptable. (ii) Respondents find almost all modern insurance practices that we described unfair. (iii) Respondents find practices for which they can influence the premium fairer. (iv) If respondents find a certain consumer characteristic illogical for basing the premium on, then respondents find using the characteristic unfair. (v) Respondents find it unfair if an insurer offers an insurance product only to a specific group. (vi) Respondents find it unfair if an insurance practice leads to the poor paying more. We also reflect on the policy implications of the findings.

This short research article interrogates the rise of digital platforms that enable ‘synthetic afterlives’, with a focus on how deathbots – AI-driven avatar interactions grounded in personal data and recordings – reshape memory practices. Drawing on socio-technical walkthroughs of four platforms – Almaya, HereAfter, Séance AI, and You, Only Virtual – we analyse how they frame, archive, and algorithmically regenerate memories. Our findings reveal a central tension: between preserving the past as a fixed archive and continually reanimating it through generative AI. Our walkthroughs demonstrate how these services commodify remembrance, reducing memory to consumer-driven interactions designed for affective engagement while obscuring the ethical, epistemological and emotional complexities of digital commemoration. In doing so, they enact reductive forms of memory that are embedded within platform economies and algorithmic imaginaries.

The integration of artificial intelligence (AI)-driven technologies into peace dialogues offers both innovative possibilities and critical challenges for contemporary peacebuilding practice. This article proposes a context-sensitive taxonomy of digital deliberation tools designed to guide the selection and adaptation of AI-assisted platforms in conflict-affected environments. Moving beyond static typologies, the framework accounts for variables such as scale, digital literacy, inclusivity, security, and the depth of AI integration. By situating digital peace dialogues within broader peacebuilding and digital democracy frameworks, the article examines how AI can enhance participation, scale deliberation, and support knowledge synthesis, —while also highlighting emerging concerns around algorithmic bias, digital exclusion, and cybersecurity threats. Drawing on case studies involving the United Nations (UN) and civil society actors, the article underscores the limitations of one-size-fits-all approaches and makes the case for hybrid models that balance AI capabilities with human facilitation to foster trust, legitimacy, and context-responsive dialogue. The analysis contributes to peacebuilding scholarship by engaging with the ethics of AI, the politics of digital diplomacy, and the sustainability of technological interventions in peace processes. Ultimately, the study argues for a dynamic, adaptive approach to AI integration, continuously attuned to the ethical, political, and socio-cultural dimensions of peacebuilding practice.