Renée Sirbu

In this paper, we examine the potential integration of artificial intelligence (AI) into capital punishment through lethal autonomous mechanisms (LAMs), an extension of developments in lethal autonomous weapons (LAWs). While most philosophical and ethical research on autonomous weapons systems focuses on warfare and policing, their potential role in capital executions remains unexplored. Through anticipatory analysis of LAMs’ integration into what we term the “death row network,” we show how retentionists might leverage these technologies to counter key abolitionist critiques. We analyze how LAMs could undermine two abolitionist arguments: the harm to corrections personnel and the medicalization of execution protocols. However, we argue that while LAMs resolve these specific challenges, they simultaneously generate new ethical concerns. First, LAMs will not eliminate the moral and psychological burden on corrections personnel entirely but rather redistribute it to networks of AI developers, software engineers, and technology professionals who become implicated in state-sanctioned killings through their work. Similarly, LAMs address the medicalization concern, yet they merely shift ethical scrutiny from the medical sector to the technology corporations developing LAMs and their industrial partners. Second, LAMs’ elimination of human presence undermines the exercise of benevolence through sustained interpersonal engagement, from sharing final conversations to offering comfort during an inmate’s last moments. This human connection, which preserves dignity even within a dehumanizing process, cannot be replicated by automated systems and is a further argument against their adoption.

Biological computing (biocomputing) leverages biologically derived materials and processes, such as DNA and protein synthesis, to perform computational tasks. Biocomputing offers significant advantages over traditional silicon-based systems in terms of scalability, energy efficiency, computational flexibility, and information storage potential. However, the distinct operational characteristics of biocomputing raise novel governance, ethical, legal, and social implications (GELSI). This article identifies and analyzes key GELSI concerns raised by biocomputing. It then highlights the inadequacies of current regulatory frameworks in addressing the unique challenges posed by massively parallel molecular computations, biosafety risks, probabilistic error management in clinical applications, patentability of biological storage systems, and ownership rights in self-replicating data systems. The analysis concludes by recommending specialized regulatory approaches and international collaboration to govern biocomputing technologies responsibly, ensuring their ethical integration and equitable benefits across global societies.

In this paper, we examine the potential integration of artificial intelligence (AI) into capital punishment through lethal autonomous mechanisms (LAMs), an extension of developments in lethal autonomous weapons (LAWs). While most philosophical and ethical research on autonomous weapons systems focuses on warfare and policing, their potential role in capital executions remains unexplored. Through anticipatory analysis of LAMs’ integration into what we term the “death row network,” we show how retentionists might leverage these technologies to counter key abolitionist critiques. We analyze how LAMs could undermine two abolitionist arguments: the harm to corrections personnel and the medicalization of execution protocols. However, we argue that while LAMs resolve these specific challenges, they simultaneously generate new ethical concerns. First, LAMs will not eliminate the moral and psychological burden on corrections personnel entirely but rather redistribute it to networks of AI developers, software engineers, and technology professionals who become implicated in state-sanctioned killings through their work. Similarly, LAMs address the medicalization concern, yet they merely shift ethical scrutiny from the medical sector to the technology corporations developing LAMs and their industrial partners. Second, LAMs’ elimination of human presence undermines the exercise of benevolence through sustained interpersonal engagement, from sharing final conversations to offering comfort during an inmate’s last moments. This human connection, which preserves dignity even within a dehumanizing process, cannot be replicated by automated systems and is a further argument against their adoption.

Brain-computer interfaces (BCIs) offer significant therapeutic opportunities for a variety of neurophysiological and neuropsychiatric disorders and may perhaps one day lead to augmenting the cognition and decision-making of the healthy brain. However, existing regulatory frameworks designed for implantable medical devices (IMDs) are inadequate to address the unique ethical, legal, and social risks associated with next-generation networked brain-computer interfaces (BCIs). In this article, we make nine recommendations to support developers in the design of BCIs and nine recommendations to support policymakers in the application of BCIs, drawing insights from the regulatory history of IMDs and principles from AI ethics. We begin by outlining the historical development of IMDs and the regulatory milestones that have shaped their oversight. Next, we summarize similarities between IMDs and emerging implantable BCIs, identifying existing provisions for their regulation. We then use two case studies of emerging cutting-edge BCIs-the HALO (Hardware Architecture for LOw-power BCIs) and SCALO (SCalable Architecture for LOw-power BCIs) computer systems-to highlight distinctive features in the design and application of nextgeneration BCIs arising from contemporary chip architectures, which necessitate reevaluating regulatory approaches. We identify critical ethical considerations for these BCIs, including unique conceptions of autonomy, identity, and mental privacy. Based on these insights, we suggest potential avenues for the ethical regulation of BCIs, emphasizing the importance of interdisciplinary collaboration and proactive mitigation of potential harms. The goal is to support the responsible design and application of new BCIs, ensuring their safe and ethical integration into medical practice.

Artificial Intelligence (AI) presents unprecedented opportunities to transform healthcare worldwide, from improving diagnostic accuracy to expanding access in underserved regions. Despite this potential and growing investment, a significant gap persists between AI’s theoretical promise and its realised benefits in healthcare settings. This article examines the complex barriers impeding AI benefits realization in global health contexts, including ethical uncertainties, data infrastructure limitations, evidence quality concerns, and regulatory ambiguities. We analyze current initiatives addressing these challenges and highlight how technological solutions alone cannot resolve fundamental healthcare inequities. Drawing on the interdisciplinary perspectives and insights presented at the Global Health in the Age of AI Symposium hosted by the Cini Foundation and Yale Digital Ethics Center, we propose five core infrastructure requirements necessary for ethical AI implementation: robust data exchange; epistemic certainty with staff autonomy; actively protected healthcare values; validated outcomes with meaningful accountability; and environmental sustainability. These requirements form the foundation for a systems approach that balances technological advancement with ethical imperatives, contextual adaptability, and global equity considerations. We conclude that the successful integration of AI into healthcare demands coordinated action across sectors and borders, with careful attention to avoiding technological colonialism and ensuring AI serves as a force for health equity rather than widening existing disparities.

Brain-computer interfaces (BCIs) show enormous potential for advancing personalized medicine. However, BCIs also introduce new avenues for cyber-attacks or security compromises. In this article, we analyze the problem and make recommendations for device manufacturers to better secure devices and to help regulators understand where more guidance is needed to protect patient safety and data confidentiality. Device manufacturers should implement the prior suggestions in their BCI products. These recommendations help protect BCI users from undue risks, including compromised personal health and genetic information, unintended BCI-mediated movement, and many other cybersecurity breaches. Regulators should mandate non-surgical device update methods, strong authentication and authorization schemes for BCI software modifications, encryption of data moving to and from the brain, and minimize network connectivity where possible. We also design a hypothetical, average-case threat model that identifies possible cybersecurity threats to BCI patients and predicts the likeliness of risk for each category of threat. BCIs are at less risk of physical compromise or attack, but are vulnerable to remote attack; we focus on possible threats via network paths to BCIs and suggest technical controls to limit network connections.