Tina Nguyen

In lieu of an abstract, here is a brief excerpt of the content:Reviewed by:Live Like Nobody Is Watching: Relational Autonomy in the Age of Artificial Intelligence Health Monitoring by Anita HoTina Nguyen (bio)Live Like Nobody Is Watching: Relational Autonomy in the Age of Artificial Intelligence Health Monitoring by Anita Ho New York: Oxford University Press, 2023As the reach of artificial intelligence (AI)- and machine learning (ML)-enabled technologies continues to expand in the healthcare field, bioethicists have examined the ethical issues that come as a result of using these technologies. In this ethical assessment, most bioethicists, myself included, have discussed how AI/ML violates the ethical principle of autonomy. The commonly cited definition of autonomy comes from Beauchamp and Childress's Principles of Biomedical Ethics (2019), where autonomy is the right of the individual to make decisions for themselves. However, autonomy regarding AI/ML in healthcare has not been or is rarely discussed from the feminist perspective. In Live Like Nobody Is Watching: Relational Autonomy in the Age of Artificial Intelligence Health Monitoring (further referred to as Live Like Nobody Is Watching), Anita Ho, in addition to other points, argues that AI for health monitoring needs to be examined through a relational autonomy lens. Not only is this perspective innovative but it also shows how a predominantly male field has shaped the narrative surrounding AI/ML. In this review, I provide a brief overview of the chapters, explore Ho's arguments for relational autonomy, and discuss future applications of this work.Live Like Nobody Is Watching begins with a preface where the readers are introduced to a real-life scenario. Ho describes a friend's experience of [End Page 101] debating with themself whether to use surveillance for their mother with early-stage dementia who claims someone has been moving her things. This sets up the rest of the book in which questions of should these technologies be used and in what context are they appropriate are debated using relational autonomy. The introduction serves as a brief primer to AI (ML and deep learning) and its role in healthcare followed by a layout of the subsequent chapters. Chapter 1 connects health monitoring AI with relational autonomy, where Ho suggests that the liberal view of autonomy is not appropriate for health monitoring technologies. This is due to the fact that the patient populations using these technologies are reliant, to various degrees, on social relationships. In chapters 2, 3, and 4, Ho addresses how health-monitoring AI requires relational autonomy through use cases concerning home-health monitoring, direct-to-consumer monitoring, and AI monitoring for medication adherence. Throughout these chapters, Ho discusses the claimed benefits of these AI/ML interventions and cautions about the ethical challenges, such as health inequities and testimonial injustice, that can arise with these technologies. Finally, chapter 5 serves as the conclusion, where Ho ultimately argues that health-monitoring AI should be viewed through relational autonomy instead of the traditional view, and she advocates for dialogic engagement (including bringing patients/patients' families into the conversation). The narrative goes full circle with the events in the preface being addressed in the epilogue, where the friend decided to incorporate a smart frame device that would allow them to have video calls with their mother. However, the friend is still grappling with the idea of surveillance given an incident in which another dementia resident accidentally wandered into their mother's room. To this, Ho ends the book by proposing better lighting in the hallways rather than immediately turning to the next high-tech innovation.Ho presents several arguments throughout the book but emphasizes the main argument that relational autonomy should be practiced over the traditional, liberal view of autonomy. Ho discusses the three elements of socio-relational autonomy (using Catriona Mackenzie's work): self-governance, self-determination, and self-authorization. Components of the traditional bioethical view of autonomy are seen in these three elements, with the process of informed consent being closely tied to the concept of autonomy. Under the traditional view, an autonomous individual should have decision-making capacity and be able to make a decision based on the information provided to them without coercion. In the informed consent process, patients should be provided adequate information along with...

In the article ‘Testimonial injustice in medical machine learning’, Pozzi argues that the prescription drug monitoring programme (PDMP) leads to testimonial injustice as physicians are more inclined to trust the PDMP’s risk scores over the patient’s own account of their medication history.1 Pozzi further develops this argument by discussing how credibility shifts from patients to machine learning (ML) systems that are supposedly neutral. As a result, a sense of distrust is now formed between patients and physicians. While there are merits to Pozzi’s main argument of epistemic injustice caused by PDMPs, Pozzi mentions but ultimately glosses over the problem of automation bias. In this commentary, I will discuss automation bias and the affect it has on clinical decision making as well as a technical problem exacerbated by the usage of PDMPs that can potentially cause physical harms. It is reiterated in the article that the confidence physicians have in the PDMP’s risk scores over the patient’s testimony leads to misplaced trust in the ML systems. What Pozzi describes here is known as automation bias, which occurs when there is an over-reliance on ML systems. …