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(1,432)
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- Faculty Publications (724)
Show Results For
- All HBS Web
(1,432)
- People (1)
- News (271)
- Research (903)
- Events (17)
- Multimedia (6)
- Faculty Publications (724)
- 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
- June 2019
- Supplement
Improving Worker Safety in the Era of Machine Learning: Practicum in Predictive Analytics
By: Michael W. Toffel and Dan Levy
- January 2024 (Revised February 2024)
- Case
Data-Driven Denim: Financial Forecasting at Levi Strauss
By: Mark Egan
The case examines Levi Strauss’ journey in implementing machine learning and AI into its financial forecasting process. The apparel company partnered with the IT company Wipro in 2017 to develop a machine learning algorithm that could help Levi Strauss forecast its... View Details
Keywords: Investor Relations; Forecasting; Machine Learning; Artificial Intelligence; Apparel; Corporate Finance; Forecasting and Prediction; AI and Machine Learning; Digital Transformation; Apparel and Accessories Industry; United States
Egan, Mark. "Data-Driven Denim: Financial Forecasting at Levi Strauss." Harvard Business School Case 224-029, January 2024. (Revised February 2024.)
- February 2018 (Revised June 2021)
- Case
New Constructs: Disrupting Fundamental Analysis with Robo-Analysts
By: Charles C.Y. Wang and Kyle Thomas
This case highlights the business challenges associated with a financial technology firm, New Constructs, that created a technology that can quickly parse complicated public firm financials to paint a clearer economic picture of firms, remove accounting distortions,... View Details
Keywords: Fundamental Analysis; Machine Learning; Robo-analysts; Financial Statements; Financial Reporting; Analysis; Information Technology; Accounting Industry; Financial Services Industry; Information Technology Industry; North America; Tennessee
Wang, Charles C.Y., and Kyle Thomas. "New Constructs: Disrupting Fundamental Analysis with Robo-Analysts." Harvard Business School Case 118-068, February 2018. (Revised June 2021.)
- June 2019
- Teaching Note
Improving Worker Safety in the Era of Machine Learning: Practicum in Predictive Analytics
By: Michael W. Toffel and Dan Levy
Teaching Note for HBS No. 618-019. View Details
- June 2019
- Supplement
Improving Worker Safety in the Era of Machine Learning: Introduction to Predictive Analytics
By: Michael W. Toffel and Dan Levy
- June 2019
- Teaching Note
Improving Worker Safety in the Era of Machine Learning: Introduction to Predictive Analytics
By: Michael W. Toffel and Dan Levy
Teaching Note for HBS No. 618-019. View Details
- Article
Detecting Adversarial Attacks via Subset Scanning of Autoencoder Activations and Reconstruction Error
By: Celia Cintas, Skyler Speakman, Victor Akinwande, William Ogallo, Komminist Weldemariam, Srihari Sridharan and Edward McFowland III
Reliably detecting attacks in a given set of inputs is of high practical relevance because of the vulnerability of neural networks to adversarial examples. These altered inputs create a security risk in applications with real-world consequences, such as self-driving... View Details
Keywords: Autoencoder Networks; Pattern Detection; Subset Scanning; Computer Vision; Statistical Methods And Machine Learning; Machine Learning; Deep Learning; Data Mining; Big Data; Large-scale Systems; Mathematical Methods; Analytics and Data Science
Cintas, Celia, Skyler Speakman, Victor Akinwande, William Ogallo, Komminist Weldemariam, Srihari Sridharan, and Edward McFowland III. "Detecting Adversarial Attacks via Subset Scanning of Autoencoder Activations and Reconstruction Error." Proceedings of the International Joint Conference on Artificial Intelligence 29th (2020).
- June 2019
- Supplement
Improving Worker Safety in the Era of Machine Learning: Practicum in Predictive Analytics
By: Michael W. Toffel and Dan Levy
- 2015
- Article
A Machine Learning Framework to Identify Students at Risk of Adverse Academic Outcomes
By: Himabindu Lakkaraju, Everaldo Aguiar, Carl Shan, David Miller, Nasir Bhanpuri, Rayid Ghani and Kecia Addison
Lakkaraju, Himabindu, Everaldo Aguiar, Carl Shan, David Miller, Nasir Bhanpuri, Rayid Ghani, and Kecia Addison. "A Machine Learning Framework to Identify Students at Risk of Adverse Academic Outcomes." Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining 21st (2015).
- April 29, 2020
- Article
The Case for AI Insurance
By: Ram Shankar Siva Kumar and Frank Nagle
When organizations place machine learning systems at the center of their businesses, they introduce the risk of failures that could lead to a data breach, brand damage, property damage, business interruption, and in some cases, bodily harm. Even when companies are... View Details
Keywords: Artificial Intelligence; Machine Learning; Internet and the Web; Safety; Insurance; AI and Machine Learning; Cybersecurity
Kumar, Ram Shankar Siva, and Frank Nagle. "The Case for AI Insurance." Harvard Business Review Digital Articles (April 29, 2020).
- Forthcoming
- Article
Beefing IT Up for Your Investor? Engagement with Open Source Communities, Innovation, and Startup Funding: Evidence from GitHub
By: Annamaria Conti, Christian Peukert and Maria P. Roche
We study the engagement of nascent firms with open source communities and its implications for innovation and attracting funding. To do so, we link data on 160,065 U.S. startups from Crunchbase to their activities on the open source software development platform... View Details
Keywords: Startups; Knowledge; Open Source Communities; GitHub; Machine Learning; Innovation; Business Startups; Venture Capital; Information Technology; Strategy
Conti, Annamaria, Christian Peukert, and Maria P. Roche. "Beefing IT Up for Your Investor? Engagement with Open Source Communities, Innovation, and Startup Funding: Evidence from GitHub." Organization Science (forthcoming). (Pre-published online, December 17, 2024.)
- Article
Who, When, and Why: A Machine Learning Approach to Prioritizing Students at Risk of Not Graduating High School on Time
By: Everaldo Aguiar, Himabindu Lakkaraju, Nasir Bhanpuri, David Miller, Ben Yuhas, Kecia Addison and Rayid Ghani
Aguiar, Everaldo, Himabindu Lakkaraju, Nasir Bhanpuri, David Miller, Ben Yuhas, Kecia Addison, and Rayid Ghani. "Who, When, and Why: A Machine Learning Approach to Prioritizing Students at Risk of Not Graduating High School on Time." Proceedings of the International Learning Analytics and Knowledge Conference 5th (2015).
- 2020
- Working Paper
Algorithm-Augmented Work and Domain Experience: The Countervailing Forces of Ability and Aversion
By: Ryan Allen and Prithwiraj Choudhury
Past research offers mixed perspectives on whether domain experience helps or hurts algorithm-augmented work performance. To reconcile these perspectives, we theorize that domain experience affects algorithm-augmented performance via two distinct countervailing... View Details
Keywords: Automation; Domain Experience; Algorithmic Aversion; Experts; Algorithms; Machine Learning; Decision-making; Future Of Work; Employees; Experience and Expertise; Decision Making; Performance
Allen, Ryan, and Prithwiraj Choudhury. "Algorithm-Augmented Work and Domain Experience: The Countervailing Forces of Ability and Aversion." Harvard Business School Working Paper, No. 21-073, October 2020. (Revised September 2021.)
- 10 Jun 2019
- Working Paper Summaries
Going Digital: Implications for Firm Value and Performance
Keywords: by Wilbur Chen and Suraj Srinivasan
- Article
Learning Models for Actionable Recourse
By: Alexis Ross, Himabindu Lakkaraju and Osbert Bastani
As machine learning models are increasingly deployed in high-stakes domains such as legal and financial decision-making, there has been growing interest in post-hoc methods for generating counterfactual explanations. Such explanations provide individuals adversely... View Details
Ross, Alexis, Himabindu Lakkaraju, and Osbert Bastani. "Learning Models for Actionable Recourse." Advances in Neural Information Processing Systems (NeurIPS) 34 (2021).
- February 2019
- Case
Miroglio Fashion (A)
By: Sunil Gupta and David Lane
Francesco Cavarero, chief information officer of Miroglio Fashion, Italy’s third-largest retailer of women’s apparel, was trying to bring analytical rigor to the company’s forecasting and inventory management decisions. But fashion is inherently hard to predict. Can... View Details
Keywords: Inventory Management; Demand Forecasting; Artificial Intelligence; Machine Learning; Forecasting and Prediction; Operations; Management; Decision Making; AI and Machine Learning; Apparel and Accessories Industry; Apparel and Accessories Industry
Gupta, Sunil, and David Lane. "Miroglio Fashion (A)." Harvard Business School Case 519-053, February 2019.
- January–February 2022
- Article
Algorithm-Augmented Work and Domain Experience: The Countervailing Forces of Ability and Aversion
By: Ryan Allen and Prithwiraj Choudhury
How does a knowledge worker’s level of domain experience affect their algorithm-augmented work performance? We propose and test theoretical predictions that domain experience has countervailing effects on algorithm-augmented performance: on one hand, domain experience... View Details
Keywords: Automation; Domain Experience; Algorithmic Aversion; Experts; Algorithms; Machine Learning; Future Of Work; Employees; Experience and Expertise; Decision Making; Performance
Allen, Ryan, and Prithwiraj Choudhury. "Algorithm-Augmented Work and Domain Experience: The Countervailing Forces of Ability and Aversion." Organization Science 33, no. 1 (January–February 2022): 149–169. ("Best PhD Student Paper" at SMS conference 2020.)
- November 1988 (Revised May 1998)
- Teaching Note
Searching for Trade Remedies: The U.S. Machine Tool Industry--1983 & United States Trade Law, Teaching Note
By: David B. Yoffie
Teaching Note for (9-388-071) and (9-387-137). View Details
- 28 Mar 2017
- Working Paper Summaries