This article identifies and examines a tension in mental health researchers' growing enthusiasm for the use of computational tools powered by advances in artificial intelligence and machine learning (AI/ML). Although there is increasing recognition of the value of participatory methods in science generally and in mental health research specifically, many AI/ML approaches, fueled by an ever-growing number of sensors collecting multimodal data, risk further distancing participants from research processes and rendering them as mere vectors or collections of data points. The imperatives of the "participatory turn" in mental health research may be at odds with the (often unquestioned) assumptions and data collection methods of AI/ML approaches. This article aims to show why this is a problem and how it might be addressed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Traditional medical artificial intelligence models that are approved for clinical use restrict themselves to single-modal data (e.g., images only), limiting their applicability in the complex, multimodal environment of medical diagnosis and treatment. Multimodal transformer models in health care can effectively process and interpret diverse data forms, such as text, images, and structured data. They have demonstrated impressive performance on standard benchmarks, like United States Medical Licensing Examination question banks, and continue to improve with scale. However, the adoption of these advanced artificial intelligence models is not without challenges. While multimodal deep learning models like transformers offer promising advancements in health care, their integration requires careful consideration of the accompanying ethical and environmental challenges.

In response to the pressing challenge of kidney allocation, characterized by growing demands for organs, this research sets out to develop a data-driven solution to this problem, which also incorporates stakeholder values. The primary objective of this study is to create a method for learning both individual and group-level preferences pertaining to kidney allocations. Drawing upon data from the 'Pairwise Kidney Patient Online Survey.' Leveraging two distinct datasets and evaluating across three levels - Individual, Group and Stability - we employ machine learning classifiers assessed through several metrics. The Individual level model predicts individual participant preferences, the Group level model aggregates preferences across participants, and the Stability level model, an extension of the Group level, evaluates the stability of these preferences over time. By incorporating stakeholder preferences into the kidney allocation process, we aspire to advance the ethical dimensions of organ transplantation, contributing to more transparent and equitable practices while promoting the integration of moral values into algorithmic decision-making.

Click to increase image sizeClick to decrease image sizeThis article refers to:Journeying to Ixtlan: Ethics of Psychedelic Medicine and Research for Alzheimer’s Disease and Related Dementias Additional informationFundingThe author(s) reported there is no funding associated with the work featured in this article.

biased AI and further the explainable AI movement: namely, the use of qualitative images, rather than quantitative data, improves communicability and makes epistemic and trust practices possible (Carusi 2008). Two-way public engagement with a diverse group of representatives is key (Bak et al. 2022) especially because images are partially subjective and Transition-Anger will often arise in people who connect most with the (missing) subject, and as such recognize the embedded inequality. Further study is needed on how this is done most effectively, and more generally, on the different ways that AI chatbots may be used to benefit bioethics.

When applying machine learning (ML) based techniques to time-series forecasting applications, there are many domain-specific considerations that can be integrated into model development to improve the likelihood of successful real-world translation. A human-centered approach, that involves end-users, has the potential to address commonly cited concerns such as algorithmic trust, explainability, and fairness. We present the DelphAI framework as an example of a practical human-centered approach to ML-based time-series forecasting for applications where end-users have little familiarity with ML techniques. The proposed socio-technical methodology incorporates essential domain knowledge through stakeholder participation into the development of predictive models. We advocate that the application of user-centered design principles can improve downstream translation and address other ethical concerns associated with ML-based forecasting.

This paper introduces a body of research on Organizational Behavior and Industrial/Organizational Psychology (OB/IO) that expands the range of empirical evidence relevant to the ongoing character-situation debate. This body of research, mostly neglected by moral philosophers, provides important insights to move the debate forward. First, the OB/IO scholarship provides empirical evidence to show that social environments like organizations have significant power to shape the character traits of their members. This scholarship also describes some of the mechanisms through which this process of reshaping character takes place. Second, the character-situation debate has narrowly focused on situational influences that affect behavior episodically and haphazardly. The OB/IO research, however, highlights the importance of distinguishing such situational influences from influences that, like organizational influences, shape our character traits because they are continuous and coordinated. Third, the OB/IO literature suggests that most individuals display character traits that, while local to the organization, can be consistent across situations. This puts pressure on the accounts of character proposed by traditional virtue ethics and situationism and provides empirical support to interactionist models based on cognitive-affective processing system theories of personality (CAPS). Finally, the OB/IO literature raises important challenges to the possibility of achieving virtue, provides valuable and untapped resources to cultivate character, and suggests new avenues of normative and empirical research.

In this chapter we critically review interdisciplinary work from philosophy, psychology, and neuroscience to shed light on perceptions of personal identity and selfhood. We review recent research that has addressed traditional philosophical questions about personal identity using empirical methods, focusing on the “moral self effect”: the finding that morality, more so than memory, is perceived to be at the core of personal identity. We raise and respond to a number of key questions and criticisms about this work. We begin by considering the operationalization of identity concepts in the empirical literature, before turning to explore the boundary conditions of “moral self effect” and how generalizable it is, and then reflecting on how this work might be connected more deeply with other neuroscience research shedding light on the self. Throughout, we highlight connections between classical themes in philosophy, psychology, and neuroscience, while also suggesting new directions for interdisciplinary collaboration.

Well before COVID-19, there was growing excitement about the potential of various digital technologies such as tele-health, smartphone apps, or AI chatbots to revolutionize mental healthcare. As the SARS-CoV-2 virus spread across the globe, clinicians warned of the mental illness epidemic within the coronavirus pandemic. Now, funding for digital mental health technologies is surging and many researchers are calling for widespread adoption to address the mental health sequelae of COVID-19. Reckoning with the ethical implications of these technologies is urgent because decisions made today will shape the future of mental health research and care for the foreseeable future. We contend that the most pressing ethical issues concern (1) the extent to which these technologies demonstrably improve mental health outcomes and (2) the likelihood that wide-scale adoption will exacerbate the existing health inequalities laid bare by the pandemic. We argue that the evidence for efficacy is weak and that the likelihood of increasing inequalities is high. First, we review recent trends in digital mental health. Next, we turn to the clinical literature to show that many technologies proposed as a response to COVID-19 are unlikely to improve outcomes. Then, we argue that even evidence-based technologies run the risk of increasing health disparities. We conclude by suggesting that policymakers should not allocate limited resources to the development of many digital mental health tools and should focus instead on evidence-based solutions to address mental health inequalities.

This special session organized by the Centre for Advancing Responsible and Ethical Artificial Intelligence (CARE-AI) consists of two 90-minute parts, focusing on two groups at the frontline of AI Ethics: students and start-up founders. Part 1 is a student-led AI Ethics paper presentation and critique: two students from the Philosophy program will present original work, "Analyzing Distrust in Human Interactions with AI," and "Enactivism and Modelling Human Behaviour in AI," (20 min); each presentation will be followed by a prepared critique from a student in the Collaborative Specialization in AI (10 min) and a 15-minute general discussion with the audience. Part 2 is an AI Ethics start-up showcase: 5 Canadian start-up companies (whose products or services either present an AI Ethics dilemma or propose a solution) will present 5-minute pitches, which will each be followed by 5 minutes of expert commentary and 5 minutes of open discussion.

Over the years, companies have adopted hiring algorithms because they promise wider job candidate pools, lower recruitment costs and less human bias. Despite these promises, they also bring perils. Using them can inflict unintentional harms on individual human rights. These include the five human rights to work, equality and nondiscrimination, privacy, free expression and free association. Despite the human rights harms of hiring algorithms, the AI ethics literature has predominantly focused on abstract ethical principles. This is problematic for two reasons. First, AI principles have been criticized for being vague and not actionable. Second, the use of vague ethical principles to discuss algorithmic risks does not provide any accountability. This lack of accountability creates an algorithmic accountability gap. Closing this gap is crucial because, without accountability, the use of hiring algorithms can lead to discrimination and unequal access to employment opportunities. This paper makes two contributions to the AI ethics literature. First, it frames the ethical risks of hiring algorithms using international human rights law as a universal standard for determining algorithmic accountability. Second, it evaluates four types of algorithmic impact assessments in terms of how effectively they address the five human rights of job applicants implicated in hiring algorithms. It determines which of the assessments can help companies audit their hiring algorithms and close the algorithmic accountability gap.

This paper explores some ways in which artificial intelligence (AI) could be used to improve human moral judgments in bioethics by avoiding some of the most common sources of error in moral judgment, including ignorance, confusion, and bias. It surveys three existing proposals for building human morality into AI: Top-down, bottom-up, and hybrid approaches. Then it proposes a multi-step, hybrid method, using the example of kidney allocations for transplants as a test case. The paper concludes with brief remarks about how to handle several complications, respond to some objections, and extend this novel method to other important moral issues in bioethics and beyond.

Well before the Covid-19 pandemic, proponents of digital psychiatry were touting the promise of various digital tools and techniques to revolutionize mental health care. As social distancing and its knock-on effects have strained existing mental health infrastructures, calls have grown louder for implementing various digital mental health solutions at scale. Decisions made today will shape the future of mental health care for the foreseeable future. Here, in hopes of countering this hype, we examine four ethical and epistemic gaps surrounding the growth of digital mental health: the evidence gap, the inequality gap, the prediction-intervention gap, and the safety gap. We argue that these gaps ought to be considered by policy-makers before society commits to a digital psychiatric future.

Recent research has begun treating the perennial philosophical question, "what makes a person the same over time?" as an empirical question. A long tradition in philosophy holds that psychological continuity and connectedness of memories are at the heart of personal identity. More recent experimental work, however, has suggested that persistence of moral character, more than memories, is perceived as essential for personal identity. While there is a growing body of evidence supporting these findings, a recent critique suggests that this research program conflates personal identity with mere similarity. To address this criticism, we explore how loss of someone's morality or memories influences perceptions of identity change and perceptions of moral duties toward the target of the change. We present participants with a classic "body switch" thought experiment and after assessing perceptions of identity persistence, we present a moral dilemma, asking participants to imagine that one of the patients must die (Study 1) or be left alone in a care home for the rest of their life (Study 2). Our results highlight the importance of the continuity of moral character, suggesting that lay intuitions are tracking (something like) personal identity, not just mere similarity.

Case reports about patients undergoing deep brain stimulation (DBS) for various motor and psychiatric disorders—including Parkinson's disease, obsessive-compulsive disorder, and treatment resistant depression—have sparked a vast literature in neuroethics. Questions about whether and how DBS changes the self have been at the fore. The present chapter brings these neuroethical debates into conversation with recent research in moral psychology. We begin in section "Clinical uses of DBS" by reviewing the recent clinical literature on DBS. In section "DBS and threats to identity," we consider whether DBS poses a threat to personal identity. In section "Surveys of judgments of identity change" we argue for engagement with recent empirical work examining judgments of when identity changes. We conclude in section "Some ethics of DBS" by highlighting a range of ethical issues raised by DBS, including various cross-cultural considerations.

This article refers to:Identifying Ethical Considerations for Machine Learning Healthcare Applications

There is little doubt that we are in the midst of such a technological revolution in the psychological sciences today, given the convergence of massive amounts of readily available data and rapid advances in computational tools and methods. We agree with Moor that as the social impact of this revolution increases, ethical issues will become more varied and more pressing. One of the areas where this is most apparent, and thus, the focus of the present chapter, is the intersection of text mining and mental health. Indeed, a recent systematic review of Machine Learning (ML) approaches to health data, containing over 100 studies, found that the most investigated problem was mental health (Yin et al., 2019). Relatedly, recent estimates suggest that between 165,000 and 325,000 health and wellness apps are now commercially available, with over 10,000 of those designed specifically for mental health (Carlo et al., 2019). The American Psychiatric Association has even developed rating systems and evaluation models for these apps (APA, 2019).

ISSN: 2329-4515 (Print) 2329-4523 (Online) Journal homepage: https://www.tandfonline.com/loi/uabr21 AI Methods in Bioethics Joshua August Skorburg, Walter Sinnott-Armstrong & Vincent Conitzer To cite this article: Joshua August Skorburg, Walter Sinnott-Armstrong & Vincent Conitzer (2020) AI Methods in Bioethics, AJOB Empirical Bioethics, 11:1, 37-39, DOI: 10.1080/23294515.2019.1706206 To link to this article: https://doi.org/10.1080/23294515.2019.1706206