Improving Worker Safety in the Era of Machine Learning (B)

Michael W. Toffel; Dan  Levy; Astrid Camille Pineda; Jose Ramon  Morales Arilla; Matthew S. Johnson

Supplement | HBS Case Collection | April 2018 (Revised February 2019)

Improving Worker Safety in the Era of Machine Learning (B)

by Michael W. Toffel, Dan Levy, Astrid Camille Pineda, Jose Ramon Morales Arilla and Matthew S. Johnson

Abstract

Supplements the (A) case.

Language: English

Format: Print

2 pages

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Citation:

Toffel, Michael W., Dan Levy, Astrid Camille Pineda, Jose Ramon Morales Arilla, and Matthew S. Johnson. "Improving Worker Safety in the Era of Machine Learning (B)." Harvard Business School Supplement 618-064, April 2018. (Revised February 2019.)

Food-borne illness affects an estimated 48 million Americans every year resulting in 3,000 deaths and the hospitalization of 128,000 people, according to the Centers for Disease Control and Prevention (CDC). Beyond that, foodborne illness in the U.S. is enormously costly, with an estimated collective annual bill of $55 billion in medical treatment, lost productivity, and lost wages, not to mention litigation expenses. Given this immense impact, the authors embarked on a research project to try to find ways to improve how local government inspectors handle their daunting task of ensuring that restaurants, hospitals, schools, and supermarkets prepare food safely. These inspections are conducted in much the same way as they have been for decades. The research suggests that they should focus on more thoughtful scheduling as a way to improve outcomes.

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Improving Worker Safety in the Era of Machine Learning (B)

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Faculty & Research

Improving Worker Safety in the Era of Machine Learning (B)

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