The COVID-19 pandemic has both exposed and created deep rifts in society. It has thrust us into deep ethical thinking to help justify the difficult decisions many will be called upon to make and to protect from decisions that lack ethical underpinnings. This paper aims to highlight ethical issues in six different areas of life highlighting the enormity of the task we are faced with globally. In the context of COVID-19, we consider health inequity, dilemmas in triage and allocation of (...) scarce resources, ethical issues associated with research, ethical considerations relating to tracing apps, and exit strategies such as immunity passports and COVID-19 vaccines. Finally, we consider environmental issues in light of COVID-19. The paper also offers some ethical reflection on these areas as many parts of the world contemplate the recovery phase. (shrink)

There is a deluge of AI-assisted decision-making systems, where our data serve as proxy to our actions, suggested by AI. The closer we investigate our data (raw input, or their learned representations, or the suggested actions), we begin to discover “bugs”. Outside of their test, controlled environments, AI systems may encounter situations investigated primarily by those in other disciplines, but experts in those fields are typically excluded from the design process and are only invited to attest to the ethical features (...) of the resulting system or to comment on demonstrations of intelligence and aspects of craftmanship after the fact. This communicative impasse must be overcome. Our idea is that philosophical and engineering considerations interact and can be fruitfully combined in the AI design process from the very beginning. We embody this idea in the role of a philosopher engineer. We discuss the role of philosopher engineers in the three main design stages of an AI system: deployment management (what is the system’s intended use, in what environment?); objective setting (what should the system be trained to do, and how?); and training (what model should be used, and why?). We then exemplify the need for philosopher engineers with an illustrative example, investigating how the future decisions of an AI-based hiring system can be fairer than those contained in the biased input data on which it is trained; and we briefly sketch the kind of interdisciplinary education that we envision will help to bring about better AI. (shrink)