Suicide is a complex act whose meanings, while sometimes tragic, vary widely. This chapter surveys the ethical landscape surrounding algorithmic methods of suicide prevention especially as it pertains to social media activity and to the moderation of online suicide communities. We begin with a typology of suicide, distinguishing between varied goals in which suicide may factor as a means. Suicides should be understood as an act with varied eliciting desires, meanings, consequences, and ethics. Further,while many suicides may be grounded on (...) irrational desire, we argue that a desire to suicide may nonetheless be rational in some cases. We then offer a survey of the attempts by social media corporations to create suicide prediction algorithms and launch interventions aimed at preventing suicide as well as an overview of the current ethical debate. We argue that the values embedded within algorithmic suicide prevention nudges are “thick” in a substantive sense and deserving of scrutiny. Given that debates about the rationality, meaning, and permissibility of suicide are common and subject to intracultural and cross-cultural differences, we maintain that the task of suicide prediction is ill-suited to algorithmic approaches. Despite an algorithm’s ability to quantify a complex phenomenon like suicide risk and its appearance of objectivity, any suicide prediction algorithm is thoroughly subjective and involves moral judgments all the way down. Any such algorithm would necessarily impose a normative stance on suicide onto its user base that is subject to reasonable intra and cross-cultural disagreement. (shrink)

While artificial womb technology is currently being studied for the purpose of improving neonatal care, I contend that this technology ought to be pursued as a means to address the unprecedented rate of unintended pregnancies. But ectogenesis, alongside other emerging reproductive technologies, is problematic insofar as it threatens to disrupt the natural link between procreation and parenthood that is normally thought to generate rights and responsibilities for biological parents. I argue that there remains only one potentially viable account of parenthood: (...) the voluntarist account, which construes parental rights as robust moral obligations that must be voluntarily undertaken. The problem is that this account mistakenly presumes a patriarchal divide between procreation and parenthood. I propose a reframing of procreation and parenthood from a feminist perspective that recognises gestational motherhood as involving robust moral obligations that ought to be voluntarily undertaken. If this were the case, all gestational mothers would be, by definition, willing mothers. To make this happen I argue that ectogenesis technology must be a widely available reproductive option. (shrink)

Broadening access to both computational and educational resources is crit- ical to diffusing machine learning (ML) innovation. However, today, most ML resources and experts are siloed in a few countries and organizations. In this article, we describe our pedagogical approach to increasing access to applied ML through a massive open online course (MOOC) on Tiny Machine Learning (TinyML). We suggest that TinyML, applied ML on resource-constrained embedded devices, is an attractive means to widen access because TinyML leverages low-cost and globally (...) accessible hardware and encourages the development of complete, self-contained applications, from data collection to deployment. To this end, a collaboration between academia and industry produced a four part MOOC that provides application-oriented instruction on how to develop solutions using TinyML. The series is openly available on the edX MOOC platform, has no prerequisites beyond basic programming, and is designed for global learners from a variety of backgrounds. It introduces real-world applications, ML algorithms, data-set engineering, and the ethi- cal considerations of these technologies through hands-on programming and deployment of TinyML applications in both the cloud and on their own microcontrollers. To facili- tate continued learning, community building, and collaboration beyond the courses, we launched a standalone website, a forum, a chat, and an optional course-project com- petition. We also open-sourced the course materials, hoping they will inspire the next generation of ML practitioners and educators and further broaden access to cutting-edge ML technologies. (shrink)