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)

According to Russell and Norvig’s (2009) classification, Artificial Intelligence (AI) is the field that aims at building systems which either think rationally, act rationally, think like humans, or...

Porsdam Mann and colleagues provide an overview of opportunities and risks associated with the use of personalized large language models (LLMs) for text production in bio)ethics (Porsdam Mann et al...

The COVID-19 pandemic has been a threat to both physical and mental health. The spreading disease and its impacts, the containment measures and the way all of our lives have dramatically changed ha...

Smartphone apps might offer a low-threshold approach to the detection of mental health conditions, such as depression. Based on the gathering of ‘passive data,’ some apps generate a user’s ‘digital phenotype,’ compare it to those of users with clinically confirmed depression and issue a warning if a depressive episode is likely. These apps can, thus, serve as epistemic tools for affected users. From an ethical perspective, it is crucial to consider epistemic injustice to promote socially responsible innovations within digital mental (...) healthcare. In cases of epistemic injustice, people are wronged specifically as epistemic agents, i.e., agents of the production and distribution of knowledge. We suggest that epistemic agency relies on different resource- and uptake-related preconditions which can be impacted by the functionality of passive self-tracking apps. We consider how this can lead to different forms of epistemic injustice (testimonial, hermeneutical, and contributory injustice) and analyze the influence of the apps’ use on epistemic practices on an individual level, in mental healthcare settings, and on the structural level. (shrink)