The current paradigm of artificial intelligence emerged as the result of a series of cultural innovations, some technical and some social. Among them are seemingly small design decisions, that led to a subtle reframing of some of the ﬁeld’s original goals, and are now accepted as standard. They correspond to technical shortcuts, aimed at bypassing problems that were otherwise too complicated or too expensive to solve, while still delivering a viable version of AI.
Far from being a series of separate problems, recent cases of unexpected eﬀects of AI are the consequences of those very choices that enabled the ﬁeld to succeed, and this is why it will be difficult to solve them. Research at the University of Bristol has considered three of these choices, investigating their connection to some of today’s challenges in AI, including those relating to bias, value alignment, privacy and explainability.