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  • October 2017 (Revised April 2018)
  • Case
  • HBS Case Collection

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

By: Michael W. Toffel, Dan Levy, Jose Ramon Morales Arilla and Matthew S. Johnson
  • Format:Print
  • | Language:English
  • | Pages:13
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Abstract

Managers make predictions all the time: How fast will my markets grow? How much inventory do I need? How intensively should I monitor my suppliers? Which potential customers will be most responsive to a particular marketing campaign? Which job candidates should I employ? Machine learning, data science, big data, and predictive analytics all use statistical techniques to predict an outcome. This case enables students to begin using data to make predictions and teaches the core metrics to evaluate how accurate predictions are. It helps students understand how to choose among alternative model specifications and introduces the concepts of overfitting and in-sample versus out-of-sample prediction. The case discussion also promotes an understanding of factors beyond prediction accuracy—such as transparency and perceived fairness—that managers need to consider when deciding which predictive algorithm to deploy. The class discussion also helps students appreciate the differences between prediction, correlation, and causation. The case protagonist recently joined a new data science team at the U.S. Occupational Safety and Health Administration (OSHA), a government agency, and needs to evaluate and recommend one of several alternative approaches that OSHA should use to improve how it targets its government inspections of workplaces to better assure safe working conditions. The case includes a dataset and exercise.

Keywords

Machine Learning; Policy Implementation; Empirical Research; Inspection; Occupational Safety; Occupational Health; Regulation; Analysis; Forecasting and Prediction; Policy; Operations; Supply Chain Management; Safety; Manufacturing Industry; Construction Industry; United States

Citation

Toffel, Michael W., Dan Levy, Jose Ramon Morales Arilla, and Matthew S. Johnson. "Improving Worker Safety in the Era of Machine Learning (A)." Harvard Business School Case 618-019, October 2017. (Revised April 2018.)
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About The Author

Michael W. Toffel

Technology and Operations Management
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Related Work

    • April 2018 (Revised February 2019)
    • Faculty Research

    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
    • October 2017 (Revised April 2018)
    • Faculty Research

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

    By: Michael W. Toffel, Dan Levy, Jose Ramon Morales Arilla and Matthew S. Johnson
    • June 2019
    • Faculty Research

    Improving Worker Safety in the Era of Machine Learning: Introduction to Predictive Analytics

    By: Michael W. Toffel and Dan Levy
    • June 2019
    • Faculty Research

    Improving Worker Safety in the Era of Machine Learning: Practicum in Predictive Analytics

    By: Michael W. Toffel and Dan Levy
Related Work
  • 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
  • Improving Worker Safety in the Era of Machine Learning (A) By: Michael W. Toffel, Dan Levy, Jose Ramon Morales Arilla and Matthew S. Johnson
  • Improving Worker Safety in the Era of Machine Learning: Introduction to Predictive Analytics By: Michael W. Toffel and Dan Levy
  • Improving Worker Safety in the Era of Machine Learning: Practicum in Predictive Analytics By: Michael W. Toffel and Dan Levy
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