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Accumulation—as both a process and a concept—has been of central importance to International Political Economy (IPE), especially for those approaches informed by Marxism and critical political economy. Whilst the concept has its roots in some of the key contributions to classical political economy, and especially that of Marx, it remains central to a rich stream of research within the field of IPE. This chapter sets out the importance and meaning of accumulation as a concept within IPE, both tracing its roots within the critical “wing” of the field, before proceeding to consider ways in which it informs ongoing research and scholarship. Accumulation, the chapter argues, is necessarily central to any attempt to delineate the scope of IPE, rightly drawing our attention towards a (the?) fundamental mechanism driving the uneven, changing, and contested expansion of global capitalism, as it develops over time.

In light of contemporary technological advancements in the fields of artificial intelligence (AI) and machine learning, coupled with significant investment in AI research and development by a range of state actors, scholars have begun considering the impact that this technology will have on international and domestic politics. While scholars express divergent views regarding the consequences of AI proliferation, this technology is likely to have a broad range of applications for international politics, from military and defense to trade and diplomacy. Recognizing the increasingly prominent role of AI in global politics, this chapter aims to provide a comprehensive overview of the opportunities and risks that the proliferation of AI technology holds for international politics by examining the factors motivating the global pursuit of this technology and evaluating its effects on authoritarianism and liberal democracy, the global balance of power, and warfare. This chapter argues that AI is best understood as an accelerating and enabling force that is less likely to produce drastic, unforeseen transformations in domestic and international politics as it is to accelerate and exacerbate trends that were already underway. As an increasingly critical tool of contemporary governance, AI is going to play a central role in future relations between international actors; it is therefore incumbent upon scholars, states, and the global community more broadly to begin preparing for international politics in the era of AI.

As has been the case for previous technological revolutions, AI will have economic and informational effects that may impact the nature and stability of democracy. In advanced democracies, AI may lead to economic transformations (such as a growing division between capital and high-skilled labor on the one hand and the rest of labor on the other, wage inequality, etc.) that may result in rising social tensions and democratic instability. However, a rise in productivity and overall growth plus the capacity of democratic governments to respond to those challenges may mitigate the negative effects of AI. AI’s effects are likely to be more strongly de-democratizing in emerging and peripheral economies. With fewer resources to adopt those new technologies, emerging and peripheral economies may be hit by the reshoring of production to advanced economies and fast deindustrialization. That may in turn reduce the kind of economic conditions (development, equality) that nurture democratic stability. AI’s economic effects may be compounded by the informational consequences of AI, which seem to reinforce the monitoring and repressive capabilities of states. After assessing the different channels through which AI could impact democracy, the chapter concludes by discussing a set of policy interventions to reconcile AI and democracy.

Artificial intelligence is already being applied in and impacting many important sectors in society, including healthcare, finance, and policing. These applications will increase as AI capabilities continue to progress, which has the potential to be highly beneficial for society, or to cause serious harm. The role of AI governance is ultimately to take practical steps to mitigate this risk of harm while enabling the benefits of innovation in AI. This requires answering challenging empirical questions about current and potential risks and benefits of AI: assessing impacts that are often widely distributed and indirect, and making predictions about a highly uncertain future. It also requires thinking through the normative question of what beneficial use of AI in society looks like, which is equally challenging. Though different groups may agree on high-level principles that uses of AI should respect (e.g., privacy, fairness, and autonomy), challenges arise when putting these principles into practice. For example, it is straightforward to say that AI systems must protect individual privacy, but there is presumably some amount or type of privacy that most people would be willing to give up to develop life-saving medical treatments. Despite these challenges, research can and has made progress on these questions. The aim of this chapter will be to give readers an understanding of this progress, and of the challenges that remain.

This chapter offers reflections and advice on AI ethics and governance from a year spent leading Partnership on AI’s multi-stakeholder Affective Computing and Ethics project, involving more than 200 engineers, scientists, lawyers, privacy and civil rights advocates, bioethicists, managers, executives, journalists, and government officials, mostly in the United States, United Kingdom, and European Union, in discussions about AI related to emotion and affect and its potential impacts on civil and human rights, with a goal of developing industry best practices and better technology policy. The author’s reflections from that year draw lessons on convening, multi-disciplinary collaboration, and affective computing and AI ethics and governance. The chapter offers a blueprint for creating the shared knowledge foundation non-technical participants need to apply their expertise. It presents question exploration as a tool for evaluating ethics risk and using “What is notice good for?” shows how a well-chosen question can serve as a catalyst for group or individual exploration of the issues, leading to insights and answers. It includes a curated list of 42 questions, catalysts for AI and ethics discussion for industry, university, news media, and policy teams; multi-disciplinary, multi-stakeholder AI ethics and governance convenings; and for individual writing and thinking, to take scholars and practitioners a step beyond or to the side of what they have been thinking about—about AI and ethics.

Regulating and governing AI will remain a challenge due to the inherent intricacy of how AI is deployed and used in practice. Regulation effectiveness and efficiency are inversely proportional to system complexity and the clarity of objectives: the more complicated an area is and the harder objectives are to operationalize, the more difficult it is to regulate and govern. Safety regulations, while often concerned with complex systems like airplanes, benefit from measurable, clear objectives and uniform subsystems. AI has emergent properties and is not just “a technology.” It is interwoven with organizations, people, and the wider social context. Furthermore, objectives like “fairness” are not only difficult to grasp and classify, but they will change their meaning case-by-case. The inherent complexity of AI systems will continue to complicate regulation and governance; however, with appropriate investment, monetary and otherwise, complexity can be tackled successfully. Due to the considerable power imbalance between users of AI in comparison to those AI systems are used on, successful regulation might be difficult to create and enforce. As such, AI regulation is more of a political and socio-economic problem than a technical one.

How do we regulate a changing technology, with changing uses, in a changing world? This chapter argues that while existing (inter)national AI governance approaches are important, they are often too siloed. Often, technology-centric approaches focus on individual AI applications, while law-centric approaches emphasize AI’s effects on pre-existing legal fields or doctrines. This chapter argues that to foster a more systematic, functional, and effective AI regulatory ecosystem, policy actors should instead complement these approaches with a regulatory perspective that emphasizes how, when, and why AI applications enable patterns of “sociotechnical change.” Drawing on theories from the emerging field of “techlaw,” it explores how this perspective can provide informed, more nuanced, and actionable perspectives on AI regulation. A focus on sociotechnical change can help analyze when and why AI applications actually create a meaningful rationale for new regulation—and how they are consequently best approached as targets for regulatory intervention, considering not just the technology, but also six distinct “problem logics” that accompany AI issues across domains. The chapter concludes by briefly sketching concrete institutional and regulatory actions that can draw on this approach to improve the regulatory triage, tailoring, timing and responsiveness, and design of AI policy.

Despite widespread skepticism of data analytics and artificial intelligence (AI) in adjudication, the Social Security Administration (SSA) pioneered path-breaking AI tools that became embedded in multiple levels of its adjudicatory process. How did this happen? What lessons can we draw from the SSA experience for AI in government? We first discuss how early strategic investments by the SSA in data infrastructure, policy, and personnel laid the groundwork for AI. Second, we document how SSA overcame a wide range of organizational barriers to develop some of the most advanced use cases in adjudication. Third, we spell out important lessons for AI innovation and governance in the public sector. We highlight the importance of leadership to overcome organizational barriers, “blended expertise” spanning technical and domain knowledge, operational data, early piloting, and continuous evaluation. AI should not be conceived of as a one-off IT product, but rather as part of continuous improvement. AI governance is quality assurance.

Artificial intelligence (AI) is poised to significantly impact healthcare systems, including clinical diagnosis, healthcare administration and delivery, and public health infrastructures. In the context of the Quintuple Aim of healthcare (patient outcomes, cost reduction, population impact, provider wellness, and equity/inclusion), this chapter discusses the current state of AI in healthcare, focusing on issues that may inform the development of adaptive, efficient, and equitable governance frameworks for AI in healthcare. The chapter introduces prominent examples of clinical AI applications in recent years, highlighting their successes and extant limitations. It emphasizes the processes of clinical AI algorithm development, implementation, and provider adoption, noting important policy considerations for active maintenance and updating of such algorithms. It also focuses on the issue of bias in AI algorithms for healthcare by (1) illustrating how unrepresentative and/or inappropriate datasets can exacerbate health disparities and inequities, and (2) emphasizing the need for diversity, transparency, and accountability in algorithm development. It provides an overview of current national and international regulatory approaches for AI-driven medical devices. It concludes with recommendations of strategic goals for developers, healthcare providers, and governmental agencies to work towards cooperatively in building a productive and equitable future for AI in healthcare.