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  • All HBS Web  (1,304)
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    • News  (247)
    • Research  (694)
    • Events  (17)
    • Multimedia  (9)
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  • 08 Oct 2018
  • Working Paper Summaries

Developing Theory Using Machine Learning Methods

Keywords: by Prithwiraj Choudhury, Ryan Allen, and Michael G. Endres
  • 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... View Details
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
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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.)
  • 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... View Details
Keywords: AI and Machine Learning; Behavior; Learning
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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.
  • October 2023 (Revised June 2024)
  • Case

ReUp Education: Can AI Help Learners Return to College?

By: Kris Ferreira, Christopher Thomas Ryan and Sarah Mehta
Founded in 2015, ReUp Education helps “stopped out students”—learners who have stopped making progress towards graduation—achieve their college completion goals. The company relies on a team of success coaches to engage with learners and help them reenroll. In 2019,... View Details
Keywords: AI; Algorithms; Machine Learning; Edtech; Education Technology; Analysis; Higher Education; AI and Machine Learning; Customization and Personalization; Failure; Education Industry; Technology Industry; United States
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Ferreira, Kris, Christopher Thomas Ryan, and Sarah Mehta. "ReUp Education: Can AI Help Learners Return to College?" Harvard Business School Case 624-007, October 2023. (Revised June 2024.)
  • Research Summary

Making Machine Learning Models Fair

By: Himabindu Lakkaraju
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. 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.... View Details
Keywords: Machine Learning; Algorithms; Fairness; Mathematical Methods
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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).
  • 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... View Details
Keywords: AI; Trends; AI and Machine Learning; Public Opinion; Technological Innovation; Competitive Advantage; Technology Industry
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Wu, Andy, and Matt Higgins. "AI Wars." Harvard Business School Teaching Note 724-482, May 2024.
  • Teaching Interest

Interpretability and Explainability in Machine Learning

By: Himabindu Lakkaraju

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

  • Research Summary

Making Machine Learning Models Interpretable

By: Himabindu Lakkaraju
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. View Details
  • August 2024
  • Background Note

Mitigating Climate Change with Machine Learning

By: Michael W. Toffel, Kelsey Carter, Amy Chambers, Avery Park and Susan Pinckney
This note highlights how machine learning is being used to decarbonize (reduce GHG emissions) several key sectors including electricity, transportation, building, industrial processes, and agriculture -- and how machine learning is being used to accelerate efforts to... View Details
Keywords: Climate; Artificial Intelligence; Adaptation; Climate Change; AI and Machine Learning; Innovation and Invention
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Toffel, Michael W., Kelsey Carter, Amy Chambers, Avery Park, and Susan Pinckney. "Mitigating Climate Change with Machine Learning." Harvard Business School Background Note 625-014, August 2024.
  • 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... View Details
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
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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

By: Srikant Datar, Apurv Jain, Charles C.Y. Wang and Siyu Zhang
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... View Details
Keywords: Big Data; Elastic Net; GDP Growth; Machine Learning; Macro Forecasting; Short Fat Data; Accounting; Economic Growth; Forecasting and Prediction; Analytics and Data Science
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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.
  • 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... View Details
Keywords: Machine Learning; Unlearning Algorithm; Mathematical Methods
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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.
  • 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. View Details
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
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Israeli, Ayelet. "PittaRosso (B): Human and Machine Learning." Harvard Business School Supplement 522-047, November 2021. (Revised December 2021.)
  • 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... View Details
Keywords: AI; Artificial Intelligence; AI and Machine Learning; Technology Adoption; Competitive Strategy; Technological Innovation
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Wu, Andy, Matt Higgins, Miaomiao Zhang, and Hang Jiang. "AI Wars." Harvard Business School Case 723-434, April 2023. (Revised February 2024.)
  • 01 Nov 2018
  • Working Paper Summaries

Forecasting Airport Transfer Passenger Flow Using Real-Time Data and Machine Learning

Keywords: by Xiaojia Guo, Yael Grushka-Cockayne, and Bert De Reyck; Air Transportation; Travel
  • 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
Keywords: Machine Learning Models; Recourse; Algorithm; Mathematical Methods
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Ross, Alexis, Himabindu Lakkaraju, and Osbert Bastani. "Learning Models for Actionable Recourse." Advances in Neural Information Processing Systems (NeurIPS) 34 (2021).
  • October 2021
  • Article

Overcoming the Cold Start Problem of CRM Using a Probabilistic Machine Learning Approach

By: Nicolas Padilla and Eva Ascarza
The success of Customer Relationship Management (CRM) programs ultimately depends on the firm's ability to understand consumers' preferences and precisely capture how these preferences may differ across customers. Only by understanding customer heterogeneity, firms can... View Details
Keywords: Customer Management; Targeting; Deep Exponential Families; Probabilistic Machine Learning; Cold Start Problem; Customer Relationship Management; Programs; Consumer Behavior; Analysis
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Padilla, Nicolas, and Eva Ascarza. "Overcoming the Cold Start Problem of CRM Using a Probabilistic Machine Learning Approach." Journal of Marketing Research (JMR) 58, no. 5 (October 2021): 981–1006.
  • 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 Feb 2018
  • Working Paper Summaries

Different Strokes for Different Folks: Experimental Evidence on Complementarities Between Human Capital and Machine Learning

Keywords: by Prithwiraj Choudhury, Evan Starr, and Rajshree Agarwal; Information Technology
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