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All HBS Web
(1,072)
- People (1)
- News (210)
- Research (542)
- Events (8)
- Multimedia (7)
- Faculty Publications (437)
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- Article
Multivariate Unsupervised Machine Learning for Anomaly Detection in Enterprise Applications
By: Daniel Elsner, Pouya Aleatrati Khosroshahi, Alan MacCormack and Robert Lagerström
Existing application performance management (APM) solutions lack robust anomaly detection capabilities and root cause analysis techniques that do not require manual efforts and domain knowledge. In this paper, we develop a density-based unsupervised machine learning...
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Keywords:
Big Data;
Data Science And Analytics Management;
Governance And Compliance;
Organizational Systems And Technology;
Anomaly Detection;
Application Performance Management;
Machine Learning;
Enterprise Architecture;
Analytics and Data Science
Elsner, Daniel, Pouya Aleatrati Khosroshahi, Alan MacCormack, and Robert Lagerström. "Multivariate Unsupervised Machine Learning for Anomaly Detection in Enterprise Applications." Proceedings of the Hawaii International Conference on System Sciences 52nd (2019): 5827–5836.
- May 2024
- Teaching Note
AI Wars
By: Andy Wu and Matt Higgins
Teaching Note for HBS Case No. 723-434. In 2024, the world was looking to Google to see what the search giant and long-time putative technical leader in artificial intelligence (AI) would do to compete in the massively hyped technology of generative AI popularized over...
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Keywords:
AI
- June 2016 (Revised August 2019)
- Case
Numenta: Inventing and (or) Commercializing AI
By: David B. Yoffie, Liz Kind and David Ben Shimol
In March 2016, Donna Dubinsky (co-founder and CEO) and Jeff Hawkins (co-founder) were struggling with a key question: Could Numenta be successful in both creating fundamental technology and building a commercial business? Located in Redwood City, CA, Numenta was...
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Keywords:
Artificial Intelligence;
Machine Intelligence;
Machine Learning;
Strategy;
Business Model;
Entrepreneurship;
Information;
Technological Innovation;
Research;
Research and Development;
Information Technology;
Applications and Software;
Technology Adoption;
Digital Platforms;
Commercialization;
AI and Machine Learning
Yoffie, David B., Liz Kind, and David Ben Shimol. "Numenta: Inventing and (or) Commercializing AI." Harvard Business School Case 716-469, June 2016. (Revised August 2019.)
- 2020
- Working Paper
Machine Learning for Pattern Discovery in Management Research
Supervised machine learning (ML) methods are a powerful toolkit for discovering robust patterns in quantitative data. The patterns identified by ML could be used as an observation for further inductive or abductive research, but should not be treated as the result of a...
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Keywords:
Machine Learning;
Theory Building;
Induction;
Decision Trees;
Random Forests;
K-nearest Neighbors;
Neural Network;
P-hacking;
Analytics and Data Science;
Analysis
Choudhury, Prithwiraj, Ryan Allen, and Michael G. Endres. "Machine Learning for Pattern Discovery in Management Research." Harvard Business School Working Paper, No. 19-032, September 2018. (Revised June 2020.)
- 08 Oct 2018
- Working Paper Summaries
Developing Theory Using Machine Learning Methods
- December 2023
- Article
Self-Orienting in Human and Machine Learning
By: Julian De Freitas, Ahmet Uğuralp, Zeliha Uğuralp, Laurie Paul, Joshua B. Tenenbaum and T. Ullman
A current proposal for a computational notion of self is a representation of one’s body in a specific time and place, which includes the recognition of that representation as the agent. This turns self-representation into a process of self-orientation, a challenging...
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De Freitas, Julian, Ahmet Uğuralp, Zeliha Uğuralp, Laurie Paul, Joshua B. Tenenbaum, and T. Ullman. "Self-Orienting in Human and Machine Learning." Nature Human Behaviour 7, no. 12 (December 2023): 2126–2139.
- April 2023 (Revised February 2024)
- Case
AI Wars
By: Andy Wu, Matt Higgins, Miaomiao Zhang and Hang Jiang
In February 2024, the world was looking to Google to see what the search giant and long-time putative technical leader in artificial intelligence (AI) would do to compete in the massively hyped technology of generative AI. Over a year ago, OpenAI released ChatGPT, a...
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Keywords:
AI;
Artificial Intelligence;
AI and Machine Learning;
Technology Adoption;
Competitive Strategy;
Technological Innovation
Wu, Andy, Matt Higgins, Miaomiao Zhang, and Hang Jiang. "AI Wars." Harvard Business School Case 723-434, April 2023. (Revised February 2024.)
- Research Summary
Making Machine Learning Models Fair
The goal of this research direction is to ensure that the machine learning models we build and deploy do not discriminate against individuals from minority groups.
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- May 2024
- Supplement
HubSpot and Motion AI (B): Generative AI Opportunities
By: Jill Avery
The technologies driving artificial intelligence (AI) had progressed significantly since HubSpot’s acquisition of Motion AI in 2017. Generative AI was the newest major development. Software-as-a-service (SaaS) companies such as HubSpot were analyzing how generative AI...
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Keywords:
Artificial Intelligence;
AI;
Customer Relationship Management;
CRM;
Chatbots;
Sales Management;
Generative Ai;
Software;
SaaS;
Marketing;
Sales;
AI and Machine Learning;
Technology Industry;
United States
Avery, Jill. "HubSpot and Motion AI (B): Generative AI Opportunities." Harvard Business School Supplement 524-088, May 2024.
- Teaching Interest
Interpretability and Explainability in Machine Learning
As machine learning models are increasingly being employed to aid decision makers in high-stakes settings such as healthcare and criminal justice, it is important to ensure that the decision makers correctly understand and consequent trust the functionality of these... View Details
- Article
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness
By: Michael J Kearns, Seth Neel, Aaron Leon Roth and Zhiwei Steven Wu
The most prevalent notions of fairness in machine learning are statistical definitions: they fix a small collection of pre-defined groups, and then ask for parity of some statistic of the classifier (like classification rate or false positive rate) across these groups....
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Kearns, Michael J., Seth Neel, Aaron Leon Roth, and Zhiwei Steven Wu. "Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness." Proceedings of the International Conference on Machine Learning (ICML) 35th (2018).
- Research Summary
Making Machine Learning Models Interpretable
I work on developing various tools and methodologies which can help decision makers (e.g., doctors, managers) to better understand the predictions of machine learning models.
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- October 2017 (Revised April 2018)
- Case
Improving Worker Safety in the Era of Machine Learning (A)
By: Michael W. Toffel, Dan Levy, Jose Ramon Morales Arilla and Matthew S. Johnson
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...
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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
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.)
- 2020
- Working Paper
Is Accounting Useful for Forecasting GDP Growth? A Machine Learning Perspective
We provide a comprehensive examination of whether, to what extent, and which accounting variables are useful for improving the predictive accuracy of GDP growth forecasts. We leverage statistical models that accommodate a broad set of (341) variables—outnumbering the...
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Keywords:
Big Data;
Elastic Net;
GDP Growth;
Machine Learning;
Macro Forecasting;
Short Fat Data;
Accounting;
Economic Growth;
Forecasting and Prediction;
Analytics and Data Science
Datar, Srikant, Apurv Jain, Charles C.Y. Wang, and Siyu Zhang. "Is Accounting Useful for Forecasting GDP Growth? A Machine Learning Perspective." Harvard Business School Working Paper, No. 21-113, December 2020.
- November 2021 (Revised December 2021)
- Supplement
PittaRosso (B): Human and Machine Learning
By: Ayelet Israeli
This case supplements the "PittaRosso: Artificial Intelligence-Driven Pricing and Promotion" case, and provides major highlights on what happened at the company since the first case.
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Keywords:
Artificial Intelligence;
Pricing;
Pricing Algorithm;
Pricing Decisions;
Pricing Strategy;
Pricing Structure;
Promotion;
Promotions;
Online Marketing;
Data-driven Decision-making;
Data-driven Management;
Retail;
Retail Analytics;
Price;
Advertising Campaigns;
Analytics and Data Science;
Analysis;
Digital Marketing;
Budgets and Budgeting;
Marketing Strategy;
Marketing;
Transformation;
Decision Making;
AI and Machine Learning;
Retail Industry;
Italy
Israeli, Ayelet. "PittaRosso (B): Human and Machine Learning." Harvard Business School Supplement 522-047, November 2021. (Revised December 2021.)
- Mar 2021
- Conference Presentation
Descent-to-Delete: Gradient-Based Methods for Machine Unlearning
By: Seth Neel, Aaron Leon Roth and Saeed Sharifi-Malvajerdi
We study the data deletion problem for convex models. By leveraging techniques from convex optimization and reservoir sampling, we give the first data deletion algorithms that are able to handle an arbitrarily long sequence of adversarial updates while promising both...
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Neel, Seth, Aaron Leon Roth, and Saeed Sharifi-Malvajerdi. "Descent-to-Delete: Gradient-Based Methods for Machine Unlearning." Paper presented at the 32nd Algorithmic Learning Theory Conference, March 2021.
- April 2024 (Revised May 2024)
- Case
Anthropic: Building Safe AI
By: Shikhar Ghosh and Shweta Bagai
In March 2024, Anthropic, a leading AI safety and research company, made headlines with the launch of Claude 3, its most advanced AI model. This marked Anthropic’s bold entry into the multimodal GenAI domain, showcasing capabilities extending to both image and text...
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Ghosh, Shikhar, and Shweta Bagai. "Anthropic: Building Safe AI." Harvard Business School Case 824-129, April 2024. (Revised May 2024.)
- 01 Nov 2018
- Working Paper Summaries
Forecasting Airport Transfer Passenger Flow Using Real-Time Data and Machine Learning
- January 2019 (Revised October 2019)
- Case
Liulishuo: AI English Teacher
By: John J-H Kim and Shu Lin
Educators and entrepreneurs alike are excited about the potential for artificial intelligence (AI) and machine learning to change the way learning will look like in the future. There is a confluence of factors such as the availability of large sources of rich,...
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Keywords:
AI;
Artificial Intelligence;
Education Technology;
Information Technology;
Education;
Entrepreneurship;
AI and Machine Learning;
Education Industry;
China
Kim, John J-H, and Shu Lin. "Liulishuo: AI English Teacher." Harvard Business School Case 319-090, January 2019. (Revised October 2019.)
- 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...
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Keywords:
Government Experimentation;
Auditing;
Inspection;
Evaluation;
Process Improvement;
Government Administration;
AI and Machine Learning;
Safety;
Governing Rules, Regulations, and Reforms
Johnson, Matthew S., David I. Levine, and Michael W. Toffel. "Making Workplaces Safer Through Machine Learning." Regulatory Review (February 26, 2024).