With automation of routine decisions coupled with more intricate and complex information architecture operating this automation, concerns are increasing about the trustworthiness of these systems. These concerns are exacerbated by a class of artificial intelligence that uses deep learning, an algorithmic system of deep neural networks, which on the whole remain opaque or hidden from human comprehension. This situation is commonly referred to as the black box problem in AI. Without understanding how AI reaches its conclusions, it is an open (...) question to what extent we can trust these systems. The question of trust becomes more urgent as we delegate more and more decision-making to and increasingly rely on AI to safeguard significant human goods, such as security, healthcare, and safety. Models that “open the black box” by making the non-linear and complex decision process understandable by human observers are promising solutions to the black box problem in AI but are limited, at least in their current state, in their ability to make these processes less opaque to most observers. A philosophical analysis of trust will show why transparency is a necessary condition for trust and eventually for judging AI to be trustworthy. A more fruitful route for establishing trust in AI is to acknowledge that AI is situated within a socio-technical system that mediates trust, and by increasing the trustworthiness of these systems, we thereby increase trust in AI. (shrink)

We discuss the relationship between expertise, expert authority, and trust in the case of vaccine research and policy, with a particular focus on COVID-19 vaccines. We argue that expert authority is not merely an epistemic notion, but entails being trusted by the relevant public and is valuable if it is accompanied by expert trustworthiness. Trustworthiness requires, among other things, being transparent, acknowledging uncertainty and expert disagreement (e.g., around vaccines’ effectiveness and safety), being willing to revise views in response to new (...) evidence, and being clear about the values that underpin expert recommendations. We explore how failure to acknowledge expert disagreement and uncertainty can undermine trust in vaccination and public health experts, using expert recommendations around COVID-19 vaccines as a case study. (shrink)