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)

The last decade has seen significant improvements in Artificial Intelligence (AI) technologies, including robotics, machine vision, speech recognition and text generation. Increasing automation will undoubtedly affect the future of work, and discussions on how the development of AI in the workplace will impact labor markets often include two scenarios: (1) labor replacement and (2) labor enabling. The former involves replacing workers with machines, while the latter assumes that human-machine cooperation can significantly improve worker productivity. In this context, it is often (...) argued that (1) could lead to mass unemployment and that (2) therefore would be more desirable. We argue, however, that the labor-enabling scenario conflates two distinct possibilities. On the one hand, technology can increase productivity while also promoting “the goods of work,” such as the opportunity to pursue excellence, experience a sense of community and contribute to society (human augmentation). On the other hand, higher productivity can also be achieved in a way that reduces opportunities for the “goods of work” and/or increases “the bads of work,” such as injury, reduced physical and mental health, reduction of autonomy, privacy and human dignity (human stunting). We outline the differences of these outcomes and discuss the implications for the labor market in the context of contemporaneous discussions on the value of work and human wellbeing. (shrink)

Algorithmic decision-making based on profiling may significantly affect people’s destinies. As a rule, however, explanations for such decisions are lacking. What are the chances for a “right to explanation” to be realized soon? After an exploration of the regulatory efforts that are currently pushing for such a right it is concluded that, at the moment, the GDPR stands out as the main force to be reckoned with. In cases of profiling, data subjects are granted the right to receive meaningful information (...) about the functionality of the system in use; for fully automated profiling decisions even an explanation has to be given. However, the trade secrets and intellectual property rights (IPRs) involved must be respected as well. These conflicting rights must be balanced against each other; what will be the outcome? Looking back to 1995, when a similar kind of balancing had been decreed in Europe concerning the right of access (DPD), Wachter et al. (2017) find that according to judicial opinion only generalities of the algorithm had to be disclosed, not specific details. This hardly augurs well for a future right of access let alone to explanation. Thereupon the landscape of IPRs for machine learning (ML) is analysed. Spurred by new USPTO guidelines that clarify when inventions are eligible to be patented, the number of patent applications in the US related to ML in general, and to “predictive analytics” in particular, has soared since 2010—and Europe has followed. I conjecture that in such a climate of intensified protection of intellectual property, companies may legitimately claim that the more their application combines several ML assets that, in addition, are useful in multiple sectors, the more value is at stake when confronted with a call for explanation by data subjects. Consequently, the right to explanation may be severely crippled. (shrink)

Critics currently argue that applied ethics approaches to artificial intelligence (AI) are too principles-oriented and entail a theory–practice gap. Several applied ethical approaches try to prevent such a gap by conceptually translating ethical theory into practice. In this article, we explore how the currently most prominent approaches of AI ethics translate ethics into practice. Therefore, we examine three approaches to applied AI ethics: the embedded ethics approach, the ethically aligned approach, and the Value Sensitive Design (VSD) approach. We analyze each (...) of these three approaches by asking how they understand and conceptualize theory and practice. We outline the conceptual strengths as well as their shortcomings: an embedded ethics approach is context-oriented but risks being biased by it; ethically aligned approaches are principles-oriented but lack justification theories to deal with trade-offs between competing principles; and the interdisciplinary Value Sensitive Design approach is based on stakeholder values but needs linkage to political, legal, or social governance aspects. Against this background, we develop a meta-framework for applied AI ethics conceptions with three dimensions. Based on critical theory, we suggest these dimensions as starting points to critically reflect on the conceptualization of theory and practice. We claim, first, that the inclusion of the dimension of affects and emotions in the ethical decision-making process stimulates reflections on vulnerabilities, experiences of disregard, and marginalization already within the AI development process. Second, we derive from our analysis that considering the dimension of justifying normative background theories provides both standards and criteria as well as guidance for prioritizing or evaluating competing principles in cases of conflict. Third, we argue that reflecting the governance dimension in ethical decision-making is an important factor to reveal power structures as well as to realize ethical AI and its application because this dimension seeks to combine social, legal, technical, and political concerns. This meta-framework can thus serve as a reflective tool for understanding, mapping, and assessing the theory–practice conceptualizations within AI ethics approaches to address and overcome their blind spots. (shrink)