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- Faculty Publications (423)
Show Results For
-
All HBS Web
(984)
- People (1)
- News (193)
- Research (533)
- Events (8)
- Multimedia (6)
- Faculty Publications (423)
- 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.
- 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.)
- 14 Mar 2023
- News
Can AI and Machine Learning Help Park Rangers Prevent Poaching?
- 08 Oct 2018
- Working Paper Summaries
Developing Theory Using Machine Learning Methods
- 21 Feb 2019
- Blog Post
Machine Learning and Behavioral Economics
This is a repost from the recruiting blog. For John Bracaglia, his academic and professional careers have been driven by two themes: “machine learning and behavioral...
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- 02–03 Dec 2022
- HBS Alumni Events
D^3 Catalyst: No Code Machine Learning and Artificial Intelligence
Do you want to delve into Machine Learning and Artificial Intelligence, but you feel overwhelmed and intimidated? Do you want to leverage the power of Machine Learning and Artificial Intelligence without writing any code? Do you want to leverage Machine Learning and...
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- 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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- 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.)
- 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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- TeachingInterests
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
- 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.
Making Workplaces Safer Through Machine Learning
Government agencies can use machine learning to improve the effectiveness of regulatory inspections. Our study found that OSHA could prevent as much as twice as many injuries—translating to up to 16,000 fewer workers injured and nearly $800 million in social... View Details
- 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.
- 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.)
- 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.)
- 2022
- Working Paper
What Would It Mean for a Machine to Have a Self?
By: Julian De Freitas, Ahmet Kaan Uğuralp, Zeliha Uğuralp, Laurie Paul, Joshua B. Tenenbaum and Tomer Ullman
What would it mean for autonomous AI agents to have a ‘self’? One proposal for a minimal
notion of self is a representation of one’s body spatio-temporally located in the world, with a tag
of that representation as the agent taking actions in the world. This turns...
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De Freitas, Julian, Ahmet Kaan Uğuralp, Zeliha Uğuralp, Laurie Paul, Joshua B. Tenenbaum, and Tomer Ullman. "What Would It Mean for a Machine to Have a Self?" Harvard Business School Working Paper, No. 23-017, September 2022.