The target article by Laacke et al. focuses on the specific context of identifying people in social media with a high risk of depression by using artificial intelligence technologies. I...

The use of black box algorithms in medicine has raised scholarly concerns due to their opaqueness and lack of trustworthiness. Concerns about potential bias, accountability and responsibility, patient autonomy and compromised trust transpire with black box algorithms. These worries connect epistemic concerns with normative issues. In this paper, we outline that black box algorithms are less problematic for epistemic reasons than many scholars seem to believe. By outlining that more transparency in algorithms is not always necessary, and by explaining that (...) computational processes are indeed methodologically opaque to humans, we argue that the reliability of algorithms provides reasons for trusting the outcomes of medical artificial intelligence. To this end, we explain how computational reliabilism, which does not require transparency and supports the reliability of algorithms, justifies the belief that results of medical AI are to be trusted. We also argue that several ethical concerns remain with black box algorithms, even when the results are trustworthy. Having justified knowledge from reliable indicators is, therefore, necessary but not sufficient for normatively justifying physicians to act. This means that deliberation about the results of reliable algorithms is required to find out what is a desirable action. Thus understood, we argue that such challenges should not dismiss the use of black box algorithms altogether but should inform the way in which these algorithms are designed and implemented. When physicians are trained to acquire the necessary skills and expertise, and collaborate with medical informatics and data scientists, black box algorithms can contribute to improving medical care. (shrink)

Volume 19, Issue 11, November 2019, Page 9-12.

Modern digital technologies—from web-based services to Artificial Intelligence (AI) solutions—increasingly affect the daily lives of billions of people. Such innovation brings huge opportunities, but also concerns about design, development, and deployment of digital technologies. This article identifies and discusses five clusters of risk in the international debate about digital ethics: ethics shopping; ethics bluewashing; ethics lobbying; ethics dumping; and ethics shirking.

Book reviewed in this article: Principles of Biomedical Ethics. By Tom L. Beauchamp and James F. Childress.

The development of artificial intelligence (AI) in medicine raises fundamental ethical issues. As one example, AI systems in the field of mental health successfully detect signs of mental disorders, such as depression, by using data from social media. These AI depression detectors (AIDDs) identify users who are at risk of depression prior to any contact with the healthcare system. The article focuses on the ethical implications of AIDDs regarding affected users’ health-related autonomy. Firstly, it presents the (ethical) discussion of AI (...) in medicine and, specifically, in mental health. Secondly, two models of AIDDs using social media data and different usage scenarios are introduced. Thirdly, the concept of patient autonomy, according to Beauchamp and Childress, is critically discussed. Since this concept does not encompass the specific challenges linked with the digital context of AIDDs in social media sufficiently, the current analysis suggests, finally, an extended concept of health-related digital autonomy. (shrink)

In an analysis of artificially intelligent systems for medical diagnostics and treatment planning we argue that patients should be able to exercise a right to withdraw from AI diagnostics and treatment planning for reasons related to (1) the physician’s role in the patients’ formation of and acting on personal preferences and values, (2) the bias and opacity problem of AI systems, and (3) rational concerns about the future societal effects of introducing AI systems in the health care sector.

Mhealth technology is mushrooming world-wide and, in a variety of forms, reaches increasing numbers of users in ever-widening contexts and virtually independent from standard medical evidence assessment. Yet, debate on the broader societal impact including in particular mapping and classification of ethical issues raised has been limited. This article, as part of an ongoing empirically informed ethical research project, provides an overview of ethical issues of mhealth applications with a specific focus on implications on autonomy as a key notion in (...) the debate. A multi-stage model of references to the potential of mhealth use for strengthening some or other form of self-determination will be proposed as a descriptive tool. It illustrates an assumed continuum of enhanced autonomy via mhealth broadly conceived: from patient to user autonomy, to improved health literacy, and finally to the vision of supra-individual empowerment and democratised, participatory health and medicine as a whole. On closer examination, however, these references are frequently ambivalent or vague, perpetuating the at times uncritical use of established autonomy concepts in medical ethics. The article suggests zooming in on the range of autonomy-related aspects against the backdrop of digital innovation and datafied health more generally, and on this basis add to existing frameworks for the ethical evaluation of mhealth more specifically. (shrink)

In “Artificial Intelligence, Social Media and Depression: A New Concept of Health-Related Digital Autonomy,” Laacke and colleagues posit a revised model of autonomy when using digital algori...

Trust constitutes a fundamental strategy to deal with risks and uncertainty in complex societies. In line with the vast literature stressing the importance of trust in doctor–patient relationships, trust is therefore regularly suggested as a way of dealing with the risks of medical artificial intelligence (AI). Yet, this approach has come under charge from different angles. At least two lines of thought can be distinguished: (1) that trusting AI is conceptually confused, that is, that we cannot trust AI; and (2) (...) that it is also dangerous, that is, that we should not trust AI—particularly if the stakes are as high as they routinely are in medicine. In this paper, we aim to defend a notion of trust in the context of medical AI against both charges. To do so, we highlight the technically mediated intentions manifest in AI systems, rendering trust a conceptually plausible stance for dealing with them. Based on literature from human–robot interactions, psychology and sociology, we then propose a novel model to analyse notions of trust, distinguishing between three aspects: reliability, competence, and intentions. We discuss each aspect and make suggestions regarding how medical AI may become worthy of our trust. (shrink)

Laacke et al. describe the emergence of novel analytical tools—artificial intelligence depression detectors —that employ artificial intelligence to predict depression. The authors the...

A mark of our modern age is the translation of the non-digital to the digital, an evolution likely only to accelerate and to demand the proactive development of robust ethical guidance to navigate...

Laacke et al. investigate the ethical implications of possible artificial intelligence systems that automatically detect signs of depression by analyzing data from social media. The art...

Digital mental health can be understood as the in situ quantification of an individual’s data from personal devices to measure human behavior in both health and disease (Huckvale, Venkatesh and Chr...

The rapid expansion of computational tools and of data science methods in healthcare has, undoubtedly, raised a whole new set of bioethical challenges. As Laacke and colleagues rightly note,...

The authors of the concept Health-Related Digital Autonomy have laid the first building block to examine the interactions between artificial intelligence, social media, and depression f...

Bioethics, Volume 36, Issue 2, Page 154-161, February 2022.

Laacke et al. argue that an extended concept of patient autonomy—Health-Related Digital Autonomy —is required to address the autonomy-related ethical challenges associated with the pot...