Consultation with Doctor Twitter: Consent Fatigue, and the Role of Developers in Digital Medical Ethics

Laacke et al. (2021) investigate the ethical implications of possible artificial intelligence (AI) systems that automatically detect signs of depression by analyzing data from social media. The article critically discusses the limits of the established notion of patient autonomy in such a scenario and proposes an adapted concept of health-related digital autonomy. The authors make a far-sighted contribution to the current discussion on the ethical requirements of digital health, which is highly relevant, especially due to the increasing number of health-related big data applications in medical and especially in non-medical contexts. The authors rightly point out that the conditions in the digital ecosystem are fundamentally different from the conditions in the medical or clinical realm, especially because there is no equivalent to the trusting doctorpatient relationship. Thus, the shift from patients to users, from the medical context to health-related digital services provided by tech companies, comes with new requirements (Schmietow and Marckmann 2019). In this context, Laacke et al. briefly discuss why principles from biomedical ethics should apply at all to the development of new diagnostic tools outside the biomedical context. The authors justify the need to adapt and apply such principles because of the medical purpose of new applications such as AI depression detectors and the sensitive nature of the data collected. Furthermore, Laacke et al. suggest that biomedical principles could, after all, complement ethical codes of developers and computer scientists. With this consideration in mind, I suggest dwelling on the proposed marriage of biomedical ethics and digital ethics, point to different rationalities of the medical and digital system, and suggest points for further consideration. With the rise of digital medicine, new actors are entering the arena of medicine and healthcare service. This includes tech companies such as platform providers, as well as specific professions such as engineers, computer scientists, bioinformaticians, and others. While the medical profession has a longstanding history of professional ethics, the aforementioned professions and disciplines have just recently turned to ethics questions. During the last years we have witnessed a rise of so-called digital ethics or AI ethics, manifesting in an explosion of guidelines for responsible design and use of data-driven technologies and AI in particular (Jobin, Ienca, and Vayena 2019). Debates on responsible digitization are now dominated by academic disciplines other than (medical) ethics (Mahieu et al. 2018). This, however, could not only jeopardize the quality of ethics research, but also leads to a neglect of established approaches. At the same time, tech companies increasingly rely on ethics advisory boards or ethics codes, which define the principles that should govern decisions of the organization and obligations toward stakeholders. Despite a variety of criticisms, principles-based approaches to ethics have been a success story during the last forty years. In medical research and practice, principlism can provide simple and easy-to-use tools for practitioners to support moral decision-making. Beauchamp and Childress’s four principles can also be chalked up as a success because they brought the basic demands of an already established medical ethos, namely beneficence and nonmaleficence (“primum non nocere”), together with modern ideas of patient autonomy as well as the requirements of justice necessary under limited resources of modern medicine. If we look at the catalogs of principles proposed in the context of digital ethics, a striking departure from the canon of biomedical ethics can be seen here. In a scoping review on the assessment of big data in the biomedical or healthcare context, Ienca et al. (2018)