Abstract
What should we do when artificial intelligence (AI) goes wrong? AI has huge potential to improve the safety of societally critical systems, such as healthcare and transport, but it also has the potential to introduce new risks and amplify existing ones. For instance, biases in widely deployed diagnostic AI systems could adversely affect the care of a large number of patients (Fraser et al. 2018), and hidden weaknesses in the perception systems of autonomous vehicles may regularly expose road users to significant risk (NTSB 2019). What are the most appropriate strategies for governing the safety of AI-based systems? One answer emerges from taking contrasting looks forwards to our imagined dystopian AI future and backwards to the progressive evolution of aviation safety.
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