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Publications

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  • All HBS Web  (762)
    • News  (222)
    • Research  (373)
    • Events  (12)
    • Multimedia  (8)
  • Faculty Publications  (295)

Show Results For

  • All HBS Web  (762)
    • News  (222)
    • Research  (373)
    • Events  (12)
    • Multimedia  (8)
  • Faculty Publications  (295)
← Page 25 of 762 Results →
  • Web

Entrepreneurial Management Awards & Honors - Faculty & Research

one of 100 Black Board Members Making a Difference by Board Prospects in 2024. Archie L. Jones : Received the 2024 Dr. James I. Cash Advancing Pathways Award. Olivia S. Kim : Winner of the 2024 Best Paper Award at the Red Rock Finance... View Details
  • February 26, 2024
  • Article

Making Workplaces Safer Through Machine Learning

By: Matthew S. Johnson, David I. Levine and Michael W. Toffel
Machine learning algorithms can dramatically improve regulatory effectiveness. This short article describes the authors' scholarly work that shows how the U.S. Occupational Safety and Health Administration (OSHA) could have reduced nearly twice as many occupational... View Details
Keywords: Government Experimentation; Auditing; Inspection; Evaluation; Process Improvement; Government Administration; AI and Machine Learning; Safety; Governing Rules, Regulations, and Reforms
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Johnson, Matthew S., David I. Levine, and Michael W. Toffel. "Making Workplaces Safer Through Machine Learning." Regulatory Review (February 26, 2024).
  • 26 Jan 2024
  • Blog Post

Career Advice from the Guests of the HBS Climate Rising Podcast

be impacted by the effects of climate change, so it is critical to have people in many areas who want to find solutions and drive forward the climate agenda. Every role will need to take climate action and every organization will need... View Details
  • 2019
  • Article

An Empirical Study of Rich Subgroup Fairness for Machine Learning

By: Michael J Kearns, Seth Neel, Aaron Leon Roth and Zhiwei Steven Wu
Kearns et al. [2018] recently proposed a notion of rich subgroup fairness intended to bridge the gap between statistical and individual notions of fairness. Rich subgroup fairness picks a statistical fairness constraint (say, equalizing false positive rates across... View Details
Keywords: Machine Learning; Fairness; AI and Machine Learning
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Kearns, Michael J., Seth Neel, Aaron Leon Roth, and Zhiwei Steven Wu. "An Empirical Study of Rich Subgroup Fairness for Machine Learning." Proceedings of the Conference on Fairness, Accountability, and Transparency (2019): 100–109.
  • January 2018 (Revised March 2019)
  • Case

Autonomous Vehicles: The Rubber Hits the Road...but When?

By: William Kerr, Allison Ciechanover, Jeff Huizinga and James Palano
The rise of autonomous vehicles has enormous implications for business and society. Despite the many headlines and significant investment in the technology by early 2019, it was still unclear when truly autonomous vehicles would be a commercial reality. Students will... View Details
Keywords: Technology Management; Artificial Intelligence; General Management; Robotics; Technological Innovation; Transportation; Disruption; Information Technology; Decision Making; AI and Machine Learning; Auto Industry; Technology Industry
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Kerr, William, Allison Ciechanover, Jeff Huizinga, and James Palano. "Autonomous Vehicles: The Rubber Hits the Road...but When?" Harvard Business School Case 818-088, January 2018. (Revised March 2019.)
  • 09 Jun 2024
  • Blog Post

The EC Formula: MBA Class of 2024 Looks Back

Professor Esty emphasized the importance of having a viewpoint backed up by data—I feel better equipped going into the working world having been forced to reckon with my values and how that applies to corporate policy issues. Why did you... View Details
  • Web

Impact Stories - Business History

Capitalism Re: Ai Hisano Expanding Horizons Re: Melanie Sheehan Chandler’s Legacies Re: Xavier Duran Intriguing Questions Re: Aditya Todi Business and Society Re: Melanie Sperling Platform for Dialogue Re:... View Details
  • February 2024
  • Teaching Note

Data-Driven Denim: Financial Forecasting at Levi Strauss

By: Mark Egan
Teaching Note for HBS Case No. 224-029. Levi Strauss & Co. (“Levi Strauss”) partnered with the IT services company Wipro to incorporate more sophisticated methods, such as machine learning, into their financial forecasting process starting in 2018. The decision to... View Details
Keywords: Forecasting; Regression; Machine Learning; Artificial Intelligence; Apparel; Corporate Finance; Forecasting and Prediction; AI and Machine Learning; Apparel and Accessories Industry; United States
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Egan, Mark. "Data-Driven Denim: Financial Forecasting at Levi Strauss." Harvard Business School Teaching Note 224-073, February 2024.
  • 2023
  • Working Paper

The Limits of Algorithmic Measures of Race in Studies of Outcome Disparities

By: David S. Scharfstein and Sergey Chernenko
We show that the use of algorithms to predict race has significant limitations in measuring and understanding the sources of racial disparities in finance, economics, and other contexts. First, we derive theoretically the direction and magnitude of measurement bias in... View Details
Keywords: Racial Disparity; Paycheck Protection Program; Measurement Error; AI and Machine Learning; Race; Measurement and Metrics; Equality and Inequality; Prejudice and Bias; Forecasting and Prediction; Outcome or Result
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Scharfstein, David S., and Sergey Chernenko. "The Limits of Algorithmic Measures of Race in Studies of Outcome Disparities." Working Paper, April 2023.
  • October–December 2022
  • Article

Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem

By: Mochen Yang, Edward McFowland III, Gordon Burtch and Gediminas Adomavicius
Combining machine learning with econometric analysis is becoming increasingly prevalent in both research and practice. A common empirical strategy involves the application of predictive modeling techniques to "mine" variables of interest from available data, followed... View Details
Keywords: Machine Learning; Econometric Analysis; Instrumental Variable; Random Forest; Causal Inference; AI and Machine Learning; Forecasting and Prediction
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Yang, Mochen, Edward McFowland III, Gordon Burtch, and Gediminas Adomavicius. "Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem." INFORMS Journal on Data Science 1, no. 2 (October–December 2022): 138–155.
  • 29 Nov 2023
  • News

A Holiday Benefit Dinner in LA; Seattle Club Revival Underway

there.” When they met up at a Global Networking Night in 2019, the two agreed they could combine Ayengar’s experience with launching the Singapore club with Weintraub’s business development skills to reboot the Seattle club. After an initial push was delayed View Details
Keywords: Margie Kelley
  • January 2021
  • Article

Machine Learning for Pattern Discovery in Management Research

By: Prithwiraj Choudhury, Ryan Allen and Michael G. Endres
Supervised machine learning (ML) methods are a powerful toolkit for discovering robust patterns in quantitative data. The patterns identified by ML could be used for exploratory inductive or abductive research, or for post-hoc analysis of regression results to detect... View Details
Keywords: Machine Learning; Supervised Machine Learning; Induction; Abduction; Exploratory Data Analysis; Pattern Discovery; Decision Trees; Random Forests; Neural Networks; ROC Curve; Confusion Matrix; Partial Dependence Plots; AI and Machine Learning
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Choudhury, Prithwiraj, Ryan Allen, and Michael G. Endres. "Machine Learning for Pattern Discovery in Management Research." Strategic Management Journal 42, no. 1 (January 2021): 30–57.
  • Web

Organizational Behavior Awards & Honors - Faculty & Research

Data, Algorithms and AI (Harvard Business Review Press, 2022) with Paul Leonardi. Ryan L. Raffaelli : Runner-up for the 2024 Best Entrepreneurship Paper Award from the Academy of Management, Organization and Management Theory Division,... View Details
  • 2022
  • Working Paper

TalkToModel: Explaining Machine Learning Models with Interactive Natural Language Conversations

By: Dylan Slack, Satyapriya Krishna, Himabindu Lakkaraju and Sameer Singh
Practitioners increasingly use machine learning (ML) models, yet they have become more complex and harder to understand. To address this issue, researchers have proposed techniques to explain model predictions. However, practitioners struggle to use explainability... View Details
Keywords: Natural Language Conversations; Predictive Models; AI and Machine Learning
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Slack, Dylan, Satyapriya Krishna, Himabindu Lakkaraju, and Sameer Singh. "TalkToModel: Explaining Machine Learning Models with Interactive Natural Language Conversations." Working Paper, 2022.
  • June 2021
  • Article

From Predictions to Prescriptions: A Data-driven Response to COVID-19

By: Dimitris Bertsimas, Léonard Boussioux, Ryan Cory-Wright, Arthur Delarue, Vassilis Digalakis Jr, Alexander Jacquillat, Driss Lahlou Kitane, Galit Lukin, Michael Lingzhi Li, Luca Mingardi, Omid Nohadani, Agni Orfanoudaki, Theodore Papalexopoulos, Ivan Paskov, Jean Pauphilet, Omar Skali Lami, Bartolomeo Stellato, Hamza Tazi Bouardi, Kimberly Villalobos Carballo, Holly Wiberg and Cynthia Zeng
The COVID-19 pandemic has created unprecedented challenges worldwide. Strained healthcare providers make difficult decisions on patient triage, treatment and care management on a daily basis. Policy makers have imposed social distancing measures to slow the disease, at... View Details
Keywords: COVID-19; Health Pandemics; AI and Machine Learning; Forecasting and Prediction; Analytics and Data Science
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Bertsimas, Dimitris, Léonard Boussioux, Ryan Cory-Wright, Arthur Delarue, Vassilis Digalakis Jr, Alexander Jacquillat, Driss Lahlou Kitane, Galit Lukin, Michael Lingzhi Li, Luca Mingardi, Omid Nohadani, Agni Orfanoudaki, Theodore Papalexopoulos, Ivan Paskov, Jean Pauphilet, Omar Skali Lami, Bartolomeo Stellato, Hamza Tazi Bouardi, Kimberly Villalobos Carballo, Holly Wiberg, and Cynthia Zeng. "From Predictions to Prescriptions: A Data-driven Response to COVID-19." Health Care Management Science 24, no. 2 (June 2021): 253–272.
  • 2023
  • Working Paper

Insufficiently Justified Disparate Impact: A New Criterion for Subgroup Fairness

By: Neil Menghani, Edward McFowland III and Daniel B. Neill
In this paper, we develop a new criterion, "insufficiently justified disparate impact" (IJDI), for assessing whether recommendations (binarized predictions) made by an algorithmic decision support tool are fair. Our novel, utility-based IJDI criterion evaluates false... View Details
Keywords: AI and Machine Learning; Forecasting and Prediction; Prejudice and Bias
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Menghani, Neil, Edward McFowland III, and Daniel B. Neill. "Insufficiently Justified Disparate Impact: A New Criterion for Subgroup Fairness." Working Paper, June 2023.
  • 2023
  • Article

M4: A Unified XAI Benchmark for Faithfulness Evaluation of Feature Attribution Methods across Metrics, Modalities, and Models

By: Himabindu Lakkaraju, Xuhong Li, Mengnan Du, Jiamin Chen, Yekun Chai and Haoyi Xiong
While Explainable Artificial Intelligence (XAI) techniques have been widely studied to explain predictions made by deep neural networks, the way to evaluate the faithfulness of explanation results remains challenging, due to the heterogeneity of explanations for... View Details
Keywords: AI and Machine Learning
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Lakkaraju, Himabindu, Xuhong Li, Mengnan Du, Jiamin Chen, Yekun Chai, and Haoyi Xiong. "M4: A Unified XAI Benchmark for Faithfulness Evaluation of Feature Attribution Methods across Metrics, Modalities, and Models." Advances in Neural Information Processing Systems (NeurIPS) (2023).
  • 06 Aug 2019
  • Cold Call Podcast

Super Bowl Ads Sell Products, but Do They Sell Brands?

road. He goes on to say, "When there's no man around, Goodyear should be." It probably shouldn't be surprising that advertisers took a chauvinistic tone for spots appearing on a game that was expected to be watched mostly by... View Details
Keywords: Advertising; Sports; Entertainment & Recreation; Media & Broadcasting
  • 2021
  • Working Paper

An Empirical Study of Time Allotment and Delays in E-commerce Delivery

By: M. Balakrishnan, MoonSoo Choi and Natalie Epstein
Problem definition: We study how having more time allotted to deliver an order affects the speed of the delivery process. Furthermore, we seek to predict orders that are likely to be delayed early in the delivery process so that actions can be taken to avoid delays.... View Details
Keywords: Logistics; E-commerce; Mathematical Methods; AI and Machine Learning; Performance Productivity
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Balakrishnan, M., MoonSoo Choi, and Natalie Epstein. "An Empirical Study of Time Allotment and Delays in E-commerce Delivery." Working Paper, December 2021.
  • December 2016 (Revised November 2017)
  • Teaching Plan

Olivia Lum: Wanting to Save the World

By: Geoffrey Jones and Ai Hisano
Teaching Plan for HBS No. 316-178. View Details
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Jones, Geoffrey, and Ai Hisano. "Olivia Lum: Wanting to Save the World." Harvard Business School Teaching Plan 317-083, December 2016. (Revised November 2017.)
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