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Publications

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  • All HBS Web  (760)
    • News  (222)
    • Research  (373)
    • Events  (12)
    • Multimedia  (8)
  • Faculty Publications  (294)

Show Results For

  • All HBS Web  (760)
    • News  (222)
    • Research  (373)
    • Events  (12)
    • Multimedia  (8)
  • Faculty Publications  (294)
← Page 25 of 760 Results →
  • Web

Student Spotlight: 2023 HCC Co-Presidents Reflect on Their Time at HBS and the Current Health Care Systems - Blog: Health Supplement

classmates–especially including the MD/MBAs–and taking classes such as Transforming Health Care Delivery with Ariel Stern. Additionally, I began developing hands-on experience of new areas within health care by starting my own venture,... 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 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).
  • 01 Dec 2022
  • News

Future Vision

created by Professor Mitch Weiss for students in The Entrepreneurial Manager. In class, students negotiate terms for a pool of VC money. The simulation draws on PitchBook data from real pitches, and it uses View Details
Keywords: Brian Kenny; Educational Support Services; Educational Services
  • 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.)
  • 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
  • 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.
  • 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.
  • 29 Nov 2023
  • News

A Holiday Benefit Dinner in LA; Seattle Club Revival Underway

Clubs News Clubs News Southern California Holiday Gala Will Boost Scholarship Fund After a brief hiatus due to the pandemic, the HBS Association of Southern California (HBSASC) is once again hosting its annual Holiday Benefit Gala on December 9 to support the HBSASC... View Details
Keywords: Margie Kelley
  • 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
  • 2021
  • Chapter

Towards a Unified Framework for Fair and Stable Graph Representation Learning

By: Chirag Agarwal, Himabindu Lakkaraju and Marinka Zitnik
As the representations output by Graph Neural Networks (GNNs) are increasingly employed in real-world applications, it becomes important to ensure that these representations are fair and stable. In this work, we establish a key connection between counterfactual... View Details
Keywords: Graph Neural Networks; AI and Machine Learning; Prejudice and Bias
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Agarwal, Chirag, Himabindu Lakkaraju, and Marinka Zitnik. "Towards a Unified Framework for Fair and Stable Graph Representation Learning." In Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence, edited by Cassio de Campos and Marloes H. Maathuis, 2114–2124. AUAI Press, 2021.
  • 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.
  • 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).
  • 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.
  • 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.
  • August 2023
  • Article

Explaining Machine Learning Models with Interactive Natural Language Conversations Using TalkToModel

By: Dylan Slack, Satyapriya Krishna, Himabindu Lakkaraju and Sameer Singh
Practitioners increasingly use machine learning (ML) models, yet models have become more complex and harder to understand. To understand complex models, researchers have proposed techniques to explain model predictions. However, practitioners struggle to use... View Details
Keywords: AI and Machine Learning; Technological Innovation; Technology Adoption
Citation
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Slack, Dylan, Satyapriya Krishna, Himabindu Lakkaraju, and Sameer Singh. "Explaining Machine Learning Models with Interactive Natural Language Conversations Using TalkToModel." Nature Machine Intelligence 5, no. 8 (August 2023): 873–883.
  • 2019
  • Working Paper

Soul and Machine (Learning)

By: Davide Proserpio, John R. Hauser, Xiao Liu, Tomomichi Amano, Alex Burnap, Tong Guo, Dokyun Lee, Randall Lewis, Kanishka Misra, Eric Schwarz, Artem Timoshenko, Lilei Xu and Hema Yoganarasimhan
Machine learning is bringing us self-driving cars, improved medical diagnostics, and machine translation, but can it improve marketing decisions? It can. Machine learning models predict extremely well, are scalable to “big data,” and are a natural fit to rich media... View Details
Keywords: Machine Learning; Technological Innovation; Marketing; AI and Machine Learning
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Proserpio, Davide, John R. Hauser, Xiao Liu, Tomomichi Amano, Alex Burnap, Tong Guo, Dokyun Lee, Randall Lewis, Kanishka Misra, Eric Schwarz, Artem Timoshenko, Lilei Xu, and Hema Yoganarasimhan. "Soul and Machine (Learning)." Harvard Business School Working Paper, No. 20-036, September 2019.
  • 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.
  • 12 Jun 2018
  • First Look

New Research and Ideas, June 12, 2018

for foreign companies to compete in Chinese markets. As these companies continued to scale by branching into new businesses, such as voice AI and self-driving vehicles, they also faced new and challenging... View Details
Keywords: Dina Gerdeman
  • 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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