In the digital age individuals face key choices about whether and how to share intimate details of their lives, “images of the body, biological data in general and diagnostic information” (Pyrrho,...

In this paper, we address the question of whether AI should be used for suicide prevention on social media data. We focus on algorithms that can identify persons with suicidal ideation based on their postings on social media platforms and investigate whether private companies like Facebook are justified in using these. To find out if that is the case, we start with providing two examples for AI-based means of suicide prevention in social media. Subsequently, we frame suicide prevention as an (...) issue of beneficence, develop two fictional cases to explore the scope of the principle of beneficence and apply the lessons learned to Facebook’s employment of AI for suicide prevention. We show that Facebook is neither acting under an obligation of beneficence nor acting meritoriously. This insight leads us to the general question of who is entitled to help. We conclude that private companies like Facebook can play an important role in suicide prevention, if they comply with specific rules which we derive from beneficence and autonomy as core principles of biomedical ethics. At the same time, public bodies have an obligation to create appropriate framework conditions for AI-based tools of suicide prevention. As an outlook we depict how cooperation between public and private institutions can make an important contribution to combating suicide and, in this way, put the principle of beneficence into practice. (shrink)

As COVID-19 spread, clinicians warned of mental illness epidemics within the coronavirus pandemic. Funding for digital mental health is surging and researchers are calling for widespread adoption to address the mental health sequalae of COVID-19. -/- We consider whether these technologies improve mental health outcomes and whether they exacerbate existing health inequalities laid bare by the pandemic. We argue the evidence for efficacy is weak and 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. (shrink)