When Machine Learning Goes Off the Rails

by Boris Babic, I. Glenn Cohen, Theodoros Evgeniou, and Sara Gerke

WHAT HAPPENS WHEN machine learning—computer programs that absorb new information and then change how they make decisions—leads to investment losses, biased hiring or lending, or car accidents? Should businesses allow their smart products and services to autonomously evolve, or should they “lock” their algorithms and periodically update them? If firms choose to do the latter, when and how often should those updates happen? And how should companies evaluate and mitigate the risks posed by those and other choices?

Across the business world, as machine-learning-based artificial intelligence permeates more and more offerings and processes, ...

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