The COVID-19 pandemic illustrates perfectly how the operation of science changes when questions of urgency, stakes, values and uncertainty collide -in the 'post-normal' regime. Well before the coronavirus pandemic, statisticians were debating how to prevent malpractice such as p-hacking, particularly when it could influence policy 1 . Now, computer modelling is in the limelight, with politicians presenting their policies as dictated by 'science' 2 . Yet there is no substantial aspect of this pandemic for which any researcher can currently provide precise, reliable numbers. Known unknowns include the prevalence and fatality and reproduction rates of the virus in Pandemic politics highlight how predictions need to be transparent and humble to invite insight, not blame.

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Decision-making on numerous aspects of our daily lives is being outsourced to machinelearning (ML) algorithms and artificial intelligence (AI), motivated by speed and efficiency in the decision process. ML approaches-one of the typologies of algorithms underpinning artificial intelligence-are typically developed as black boxes. The implication is that ML code scripts are rarely scrutinised; interpretability is usually sacrificed in favour of usability and effectiveness. Room for improvement in practices associated with programme development have also been flagged along other dimensions, including inter alia fairness, accuracy, accountability, and transparency. In this contribution, the production of guidelines and dedicated documents around these themes is discussed. The following applications of AIdriven decision-making are outlined: (a) risk assessment in the criminal justice system, and (b) autonomous vehicles, highlighting points of friction across ethical principles. Possible ways forward towards the implementation of governance on AI are finally examined.