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When Machine Learning Goes Off the Rails

February 1, 2021

This article does an excellent job of defining of how machine learning is a new paradigm for risk management in organizations. Although the background and examples are from healthcare and medical devices, authors I. Glenn Cohen and collaborators abstract those learnings into sound advice for all business practitioners. They anticipate the pace of technological change and broader use across industries to increase risk, observing that "accidents or unlawful decisions can occur even without negligence on anyone’s part" simply because of the nature of ML. Given that reality, the appropriate responses companies should take include:

  • Conceive of ML systems as living entities and manage them accordingly.
  • Create policies and controls around how much and which models are allowed to evolve.
  • Act ahead of emerging regulation to ensure systems are fair, safe, and efficacious.
  • Develop internal standards and processes for certification of ML before putting it into production.
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Risks & Liability