This article is a commentary on the relationship between artificial intelligence (AI), capitalism, and memory. The political policies of neoliberalism have reduced the capacity of individuals and groups to reflect on and change the social world, meanwhile applications of AI and algorithmic technologies, rooted in the profit-seeking objectives of global capitalism, deepen this deficit. In these conditions, memory in individuals and across society is at risk of becoming myopic. In this article, I develop the concept of myopic memory with two core claims. Firstly, I argue that AI is a technological development that cannot be divorced from the capitalist conditions from which it comes from and is implemented in service of. To this end, I reveal capitalism and colonialism's historical and contemporary use of surveillance as a way to control the populations it oppresses, imagining their pasts to determine their futures, disempowering them in the process. My second core claim emphasises that this process of disempowerment is undergoing an acute realisation four decades into the period of neoliberalism. Neoliberal policies have restructured society on the basis of being an individual consumer, leaving little time, space, and institutional capacity for citizens to reflect on their impact or challenge their dominance. As a result, with the growing role of AI and algorithmic technologies in shaping our engagement with society along similar lines of individualism, it is my conclusion that the scope of memory is being reduced and constrained within the prism of capitalism, reducing its potential, and rendering it myopic.

Eight major supply chains contribute to more than 50% of the global greenhouse gas emissions (GHG). These supply chains range from raw materials to end-product manufacturing. Hence, it is critical to accurately estimate the carbon footprint of these supply chains, identify GHG hotspots, explain the factors that create the hotspots, and carry out what-if analysis to reduce the carbon footprint of supply chains. Towards this, we propose an enterprise decarbonization accelerator framework with a modular structure that automates carbon footprint estimation, identification of hotspots, explainability, and what-if analysis to recommend measures to reduce the carbon footprint of supply chains. To illustrate the working of the framework, we apply it to the cradle-to-gate extent of the palm oil supply chain of a leading palm oil producer. The framework identified that the farming stage is the hotspot in the considered supply chain. As the next level of analysis, the framework identified the hotspots in the farming stage and provided explainability on factors that created hotspots. We discuss the what-if scenarios and the recommendations generated by the framework to reduce the carbon footprint of the hotspots and the resulting impact on palm oil tree yield.

This paper explores the necessary adaptations to the theory of administrative discretion when using AI systems. Regulatory frameworks in the EU, US, and Spain do not prohibit the application of AI in discretionary decision-making. Particularly, AI systems can be used when discretionary power involves correlations. However, to meet Rule of Law conditions, it is essential to establish adaptations and boundaries in areas such as duty of care, reason-giving, and judicial review. These conditions should focus on the impact of decisions on the affected individuals.

This paper aims at exploring the dynamic interplay between advanced technological developments in AI and Big Data and the sustained relevance of theoretical frameworks in scientific inquiry. It questions whether the abundance of data in the AI era reduces the necessity for theory or, conversely, enhances its importance. Arguing for a synergistic approach, the paper emphasizes the need for integrating computational capabilities with theoretical insight to uncover deeper truths within extensive datasets. The discussion extends into computational social science, where elements from sociology, psychology, and economics converge. The application of these interdisciplinary theories in the context of AI is critically examined, highlighting the need for methodological diversity and addressing the ethical implications of AI-driven research. The paper concludes by identifying future trends and challenges in AI and computational social science, offering a call to action for the scientific community, policymakers, and society. Being positioned at the intersection of AI, data science, and social theory, this paper illuminates the complexities of our digital era and inspires a re-evaluation of the methodologies and ethics guiding our pursuit of knowledge.

This early-stage research article intends to explore the regulatory and liability requirements of a not yet fully developed subset of consumer Internet of Things (IoT) objects: the mixed-functions IoT devices. These objects could be wearables or not but could perform an e-health function, such as measuring your heartbeat, as well as consumer functions, such as displaying chat notifications. I argue that these mixed-functions devices will play an important role within smart homes as they will interact with new medical IoT devices to carry on rehabilitation and other medical functions at home. That is why it is important to start mapping down all the regulatory and liability requirements that might interest mixed-functions IoT device developers for them to understand which thread to follow in this regulatory and liability requirements maze.

Artificial intelligence (AI) recently had its “iPhone moment” and adoption has drastically accelerated. Quantum computing appears poised to follow suit over the next years. However, while there has been discourse about how to use AI responsibly, there is still little appreciation and awareness among executives, managers and practitioners about the broader ethical questions and implications raised by the intersection of these emerging technologies. In this article, it is highlighted why quantum computing and AI ethics must be taken seriously by businesspersons and how these technologies affect strategic decisions; moreover, recommendations and action areas are formulated.

The integration of ‘AI’ technologies into weapon systems introduces a complex dimension to international relations and security, championing technological solutions for enduring warfare challenges, notably enhancing ‘situational awareness’ through advances such as automated ‘vision’. However, the discourse, particularly in Western militaries like that of the United States, often overlooks inherent limitations and issues in AI-based warfare. This paper explores ‘AI’s’ implications for military vision by inter alia scrutinising the US military’s Joint All-Domain Command and Control (JADC2) process. It argues that the US military actively transforms the observation, decision, and action apparatus, progressively substituting human vision and decision-making, leading to a multidimensional de-visualisation. This denotes fundamental changes in human perception, reshaping knowledge, control, and agency dynamics. In conclusion, the paper suggests an imminent era of de-visualisation in the military – a deliberate relinquishment of human control for perceived military efficiency and effectiveness. This marks a transformative shift, urging nuanced consideration of the profound impact of ‘AI’ technologies on warfare dynamics.

Intelligent machines – from automated robots to algorithmic systems – can create images and poetry, steer our preferences, aid decision making, and kill. Our perception of their capacities, relative autonomy, and moral status will profoundly affect not only how we interpret and address practical problems in world politics over the next 50 years but also how we prescribe and evaluate individual and state responses. In this article, I argue that we must analyse this emerging synthetic agency in order to effectively navigate – and theorise – the future of world politics. I begin by outlining the ways that agency has been under-theorised within the discipline of International Relations (IR) and suggest that artificial intelligence (AI) disrupts prevailing conceptions. I then examine how individual human beings and formal organisations – purposive actors with which IR is already familiar – qualify as moral agents, or bearers of duties, and explore what criteria intelligent machines would need to meet to also qualify. After demonstrating that synthetic agents currently lack the ‘reflexive autonomy’ required for moral agency, I turn to the context of war to illustrate how insights drawn from this comparative analysis counter our tendency to elide different manifestations of moral agency in ways that erode crucial notions of responsibility in world politics.

This paper motivates institutional epistemic trust as an important ethical consideration informing the responsible development and implementation of artificial intelligence (AI) technologies (or AI-inclusivity) in healthcare. Drawing on recent literature on epistemic trust and public trust in science, we start by examining the conditions under which we can have institutional epistemic trust in AI-inclusive healthcare systems and their members as providers of medical information and advice. In particular, we discuss that institutional epistemic trust in AI-inclusive healthcare depends, in part, on the reliability of AI-inclusive medical practices and programs, its knowledge and understanding among different stakeholders involved, its effect on epistemic and communicative duties and burdens on medical professionals and, finally, its interaction and alignment with the public’s ethical values and interests as well as background sociopolitical conditions against which AI-inclusive healthcare systems are embedded. To assess the applicability of these conditions, we explore a recent proposal for AI-inclusivity within the Dutch Newborn Screening Program. In doing so, we illustrate the importance, scope, and potential challenges of fostering and maintaining institutional epistemic trust in a context where generating, assessing, and providing reliable and timely screening results for genetic risk is of high priority. Finally, to motivate the general relevance of our discussion and case study, we end with suggestions for strategies, interventions, and measures for AI-inclusivity in healthcare more widely.