Principles of engineering safety: Risk and uncertainty reduction

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How researchers see this paper

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On the Safety of Machine Learning: Cyber-Physical Systems, Decision Sciences, and Data Products (2016, background): “Four categories of approache s have been identified for promoting safety in general [10]: inherently safe design, safety reserves, safe fail, and pro cedural safeguards.”
Increasing Trust in AI Services through Supplier's Declarations of Conformity (2018, background): “One may map four general categories of strategies to achieve safety proposed in [33] to machine learning [2]: inherently safe design, safety reserves, safe fail, and procedural safeguards, all of which serve to reduce epistemic uncertainty.”
FactSheets: Increasing trust in AI services through supplier's declarations of conformity (2019, background): “One may map four general categories of strategies to achieve safety proposed in [38] to machine learning [3]: inherently safe design, safety reserves, safe fail, and procedural safeguards, all of which serve to reduce epistemic uncertainty.”
FactSheets: Increasing Trust in AI Services through Supplier's Declarations of Conformity. (2018, background): “[38] N.”
Engineering safety in machine learning (2016, background): “Predictions made by machine learning systems in applications such as medical diagnosis [8], loan approval [9], and prison sentencing [10] can have a profound effect on people; undesired outcomes are truly harmful in a human sense.”