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.

The nexus of artificial intelligence (AI) and memory is typically theorized as a ‘hybrid’ or ‘symbiosis’ between humans and machines. The dangers related to this nexus are subsequently imagined as tilting the power balance between its two components, such that humanity loses control over its perception of the past to the machines. In this article, I propose a new interpretation: AI, I posit, is not merely a non-human agency that changes mnemonic processes, but rather a window through which the past itself gains agency and extends into the present. This interpretation holds two advantages. First, it reveals the full scope of the AI–memory nexus. If AI is an interactive extension of the past, rather than a technology acting upon it, every application of it constitutes an act of memory. Second, rather than locating AI’s power along familiar axes – between humans and machines, or among competing social groups – it reveals a temporal axis of power: between the present and the past. In the article’s final section, I illustrate the utility of this approach by applying it to the legal system’s increasing dependence on machines, which, I claim, represents not just a technical but a mnemonic shift, where the present is increasingly falling under the dominion of the past – embodied by AI.

The recent phenomenon of anthropomorphizing artificial intelligences (AIs) is uniquely provocative for philosophy of religion because of its tendency to place AIs in an analogous position to divinity vis-à-vis humans in spite of AIs being human artefacts. In the case of divinity, intelligent mental capacities are, and in the case of AIs are sometimes presented as inevitably becoming, not just equivalent but in fact superior to their realization in humans. Philosophers of religion would do well to learn from discussions of anthropomorphism in AI, in conversation with the historical debates over anthropomorphizing divinity, and to remember that evolved cognitive biases may lose their adaptive functions as the cultural context shifts, and even become maladaptive.

At the London Tech Week event in early June, Nvidia CEO Jensen Huang praised the UK as the ‘envy of the world’ when it comes to AI researchers, but he also criticised it as the largest AI ecosystem in the world without its own infrastructure. The criticism is somewhat self-serving: when the UK does get around to building out that infrastructure, it’s certain to consist largely of chips sold by Huang’s company. It’s also unsurprising: Huang has been pitching the idea of ‘sovereign AI’ since at least 2023, conscious that nation states are the next deep pockets to target after the hyperscalers and generously funded model builders. In a world where the only real contenders in the race for AI supremacy are the US and China, we look at how the pursuit of AI sovereignty is playing out across the rest of the planet.

Recent developments in artificial intelligence (AI) in general, and Generative AI (GenAI) in particular, have brought about changes across the academy. In applied linguistics, a growing body of work is emerging dedicated to testing and evaluating the use of AI in a range of subfields, spanning language education, sociolinguistics, translation studies, corpus linguistics, and discourse studies, inter alia. This paper explores the impact of AI on applied linguistics, reflecting on the alignment of contemporary AI research with the epistemological, ontological, and ethical traditions of applied linguistics. Through this critical appraisal, we identify areas of misalignment regarding perspectives on knowing, being, and evaluating research practices. The question of alignment guides our discussion as we address the potential affordances of AI and GenAI for applied linguistics as well as some of the challenges that we face when employing AI and GenAI as part of applied linguistics research processes. The goal of this paper is to attempt to align perspectives in these disparate fields and forge a fruitful way ahead for further critical interrogation and integration of AI and GenAI into applied linguistics.

The advent of new technologies, particularly artificial intelligence (AI), has expanded the array of options and enhanced performance in addressing biothreats. This article provides a comprehensive overview of the specific applications of AI in addressing biothreats, aiming to inform and enhance future practices. Research indicates that AI has significantly contributed to infectious disease surveillance and emergency responses, as well as bioterrorism mitigation; despite its limitations, it merits ongoing attention for further study and exploration. The effective deployment of next-generation AI in mitigating biothreats will largely hinge on our ability to engage in continuous experiential learning, acquire high-quality data, refine algorithms, and iteratively update practices. Meanwhile, it is essential to assess the operational risks associated with AI in the context of biothreats and develop robust solutions to mitigate potential risks.