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Machine learning components are enabling advances in self-driving cars, the power grid, and robotic medicine, but what are the implications for safety?
Decades of research and practice in safety engineering have created rigorous approaches to design, model, and analyze systems that meet stringent safety criteria, but extending such techniques to include machine learning components brings new challenges.
This National Academies’ report explores ways to design machine learning algorithms that align with safety engineering standards, noting that this will require changes in research, training, and engineering practice—and a shift away from focusing on the performance of machine learning algorithms in isolation.
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