Intellectual diary of a thinker of the school of Logical Positivism showing the day-by-day development of his philosophical ideas.

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)

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

This article reports the findings of AI4People, an Atomium—EISMD initiative designed to lay the foundations for a “Good AI Society”. We introduce the core opportunities and risks of AI for society; present a synthesis of five ethical principles that should undergird its development and adoption; and offer 20 concrete recommendations—to assess, to develop, to incentivise, and to support good AI—which in some cases may be undertaken directly by national or supranational policy makers, while in others may be led by other (...) stakeholders. If adopted, these recommendations would serve as a firm foundation for the establishment of a Good AI Society. (shrink)

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

The term ‘responsible AI’ has been coined to denote AI that is fair and non-biased, transparent and explainable, secure and safe, privacy-proof, accountable, and to the benefit of mankind. Since 2016, a great many organizations have pledged allegiance to such principles. Amongst them are 24 AI companies that did so by posting a commitment of the kind on their website and/or by joining the ‘Partnership on AI’. By means of a comprehensive web search, two questions are addressed by this study: (...) Did the signatory companies actually try to implement these principles in practice, and if so, how? What are their views on the role of other societal actors in steering AI towards the stated principles? It is concluded that some three of the largest amongst them have carried out valuable steps towards implementation, in particular by developing and open sourcing new software tools. To them, charges of mere ‘ethics washing’ do not apply. Moreover, some 10 companies from both the USA and Europe have publicly endorsed the position that apart from self-regulation, AI is in urgent need of governmental regulation. They mostly advocate focussing regulation on high-risk applications of AI, a policy which to them represents the sensible middle course between laissez-faire on the one hand and outright bans on technologies on the other. The future shaping of standards, ethical codes, and laws as a result of these regulatory efforts remains, of course, to be determined. (shrink)

There is a death by suicide in the world every 40 seconds, and suicide is the second leading cause of death for 15–29-year-olds. Experts say that one of the best ways to prevent suicide is for those in distress to hear from people who care about them. Facebook is in a unique position—through its support for networks and friendships on the site—to help connect a person in these difficult situations with people who can support them. Connecting people with the resources (...) they need is part of Facebook’s ongoing efforts to help build a safe community inside and outside of Facebook. This article provides a brief overview of how Facebook’s work to develop suicide prevention tools started and evolved, and the ethical considerations which surfaced during the process in the form of concrete product decisions around the implementation of these tools. This article is structured into three sections. Section 1 reviews what has been done in this space and lists and briefly describes other suicide prevention apps and tools. Section 2 describes Facebook’s overall approach to suicide prevention. Here, we’ll delve first into how that approach originated and how it was influenced by the external community’s proactive interactions with Facebook, highlighting our unique position to help address the problem. Afterwards, we’ll explain how that approach evolved, describing its various stages and iterations: understanding, reactive reporting, queue prioritization, and proactive reporting. This section describes the tools and resources Facebook has developed for people who may be at risk. Particular attention is devoted to the use of ArtificiaI Intelligence and Machine Learning to detect posts or live videos where someone might be expressing thoughts of suicide. Section 3 will elaborate on the ethical questions addressed when developing our approach and when making concrete product decisions to implement our suicide prevention tools. In this last section, we’ll expound the competing values and interests that were at stake during the product development process, and how we reached ethical balances between them. (shrink)