Machine Learning Models - Publications - Faculty & Research - Harvard Business School

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14 Mar 2023
News

Can AI and Machine Learning Help Park Rangers Prevent Poaching?

2025
Working Paper

Dynamic Personalization with Multiple Customer Signals: Multi-Response State Representation in Reinforcement Learning

By: Liangzong Ma, Ta-Wei Huang, Eva Ascarza and Ayelet Israeli

Reinforcement learning (RL) offers potential for optimizing sequences of customer interactions by modeling the relationships between customer states, company actions, and long-term value. However, its practical implementation often faces significant challenges.... View Details

Keywords: Dynamic Policy; Deep Reinforcement Learning; Representation Learning; Dynamic Difficulty Adjustment; Latent Variable Models; Customer Relationship Management; Customer Value and Value Chain; Foreign Direct Investment; Analytics and Data Science

October 2018
Article

The Operational Value of Social Media Information

By: Ruomeng Cui, Santiago Gallino, Antonio Moreno and Dennis J. Zhang

While the value of using social media information has been established in multiple business contexts, the field of operations and supply chain management have not yet explored the possibilities it offers in improving firms' operational decisions. This study attempts to... View Details

Keywords: Machine Learning; Information; Sales; Forecasting and Prediction; Social Media

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2025
Article

Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments

By: Kosuke Imai and Michael Lingzhi Li

Researchers are increasingly turning to machine learning (ML) algorithms to investigate causal heterogeneity in randomized experiments. Despite their promise, ML algorithms may fail to accurately ascertain heterogeneous treatment effects under practical settings with... View Details

Keywords: AI and Machine Learning; Mathematical Methods; Analytics and Data Science

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2023
Working Paper

Auditing Predictive Models for Intersectional Biases

By: Kate S. Boxer, Edward McFowland III and Daniel B. Neill

Predictive models that satisfy group fairness criteria in aggregate for members of a protected class, but do not guarantee subgroup fairness, could produce biased predictions for individuals at the intersection of two or more protected classes. To address this risk, we... View Details

Keywords: Predictive Models; Bias; AI and Machine Learning

February 2018 (Revised March 2018)
Case

Artificial Intelligence and the Machine Learning Revolution in Finance: Cogent Labs and the Google Cloud Platform (GCP)

By: Lauren Cohen, Christopher Malloy and William Powley

This case examines the intersection of two firms (Cogent Labs—a machine learning software firm in Tokyo; and Google, the technology infrastructure giant) attempting to exploit the benefits of artificial intelligence and machine learning in the financial services... View Details

Keywords: Technological Innovation; Finance; Growth and Development Strategy; Business Model; Applications and Software; Infrastructure; Technology Industry; Financial Services Industry

November 25, 2016
Article

How to Tell If Machine Learning Can Solve Your Business Problem

By: Anastassia Fedyk

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July 2006
Article

Dynamic Mixed Duopoly: A Model Motivated by Linux vs. Windows

By: Ramon Casadesus-Masanell and Pankaj Ghemawat

This paper analyzes a dynamic mixed duopoly in which a profit-maximizing competitor interacts with a competitor that prices at zero (or marginal cost), with the cumulation of output affecting their relative positions over time. The modeling effort is motivated by... View Details

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May 2022 (Revised July 2022)
Supplement

AWS and Amazon SageMaker (C): The Commercialization of Machine Learning Services

By: Karim R. Lakhani, Shane Greenstein and Kerry Herman

May 2022
Supplement

AWS and Amazon SageMaker (B): The Commercialization of Machine Learning Services

By: Karim R. Lakhani, Shane Greenstein and Kerry Herman

January 2015 (Revised April 2015)
Case

Zeal: Launching Personalized and Social Learning

By: John J-H Kim and Christine S. An

Set in 2014, this case follows John Danner and his team at Zeal as they consider their product development strategy. In February 2013, serial entrepreneurs John Danner and Sanjay Noronha co-found Zeal, an education technology start up providing a web-based, mobile... View Details

Keywords: Entrepreneurship; Education Technology; MVP; Product Development; Product Market Fit; Monetization Strategy; SaaS Business Models; Education; Personalized Learning

2022
Working Paper

Product2Vec: Leveraging Representation Learning to Model Consumer Product Choice in Large Assortments

By: Fanglin Chen, Xiao Liu, Davide Proserpio and Isamar Troncoso

We propose a method, Product2Vec, based on representation learning, that can automatically learn latent product attributes that drive consumer choices, to study product-level competition when the number of products is large. We demonstrate Product2Vec’s... View Details

Keywords: Consumer Choice; Consumer Behavior; Competition; Product Marketing

May 2022
Case

AWS and Amazon SageMaker (A): The Commercialization of Machine Learning Services

By: Karim R. Lakhani, Shane Greenstein and Kerry Herman

The Experimentation Machine

Leverage AI to be a 10x Founder

Today’s most successful founders know that the startups that learn the fastest will win. In The Experimentation Machine, HBS professor, entrepreneur, and venture capitalist Jeffrey J. Bussgang reveals... View Details

October 2023
Article

Improving Regulatory Effectiveness Through Better Targeting: Evidence from OSHA

By: Matthew S. Johnson, David I. Levine and Michael W. Toffel

We study how a regulator can best target inspections. Our case study is a U.S. Occupational Safety and Health Administration (OSHA) program that randomly allocated some inspections. On average, each inspection averted 2.4 serious injuries (9%) over the next five years.... View Details

Keywords: Safety Regulations; Regulations; Regulatory Enforcement; Machine Learning Models; Safety; Operations; Service Operations; Production; Forecasting and Prediction; Decisions; United States

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2021
Working Paper

Deep Learning for Two-Sided Matching

By: Sai Srivatsa Ravindranatha, Zhe Feng, Shira Li, Jonathan Ma, Scott Duke Kominers and David Parkes

We initiate the use of a multi-layer neural network to model two-sided matching and to explore the design space between strategy-proofness and stability. It is well known that both properties cannot be achieved simultaneously but the efficient frontier in this design... View Details

Keywords: Strategy-proofness; Deep Learning; Two-Sided Platforms; Marketplace Matching; Balance and Stability

Article

Development of a Deep Learning Algorithm for Periapical Disease Detection in Dental Radiographs

By: Michael G. Endres, Florian Hillen, Marios Salloumis, Ahmad R. Sedaghat, Stefan M. Niehues, Olivia Quatela, Henning Hanken, Ralf Smeets, Benedicta Beck-Broichsitter, Carsten Rendenbach, Karim R. Lakhani, Max Helland and Robert A. Gaudin

Periapical radiolucencies, which can be detected on panoramic radiographs, are one of the most common radiographic findings in dentistry and have a differential diagnosis including infections, granuloma, cysts, and tumors. In this study, we seek to investigate the... View Details

Keywords: Artificial Intelligence; Diagnosis; Computer-assisted; Image Interpretation; Machine Learning; Radiography; Panoramic Radiograph; AI and Machine Learning

2019
Working Paper

Machine Learning Approaches to Facial and Text Analysis: Discovering CEO Oral Communication Styles

By: Prithwiraj Choudhury, Dan Wang, Natalie A. Carlson and Tarun Khanna

We demonstrate how a novel synthesis of three methods—(1) unsupervised topic modeling of text data to generate new measures of textual variance, (2) sentiment analysis of text data, and (3) supervised ML coding of facial images with a cutting-edge convolutional neural... View Details

Keywords: Spoken Communication; Business History; Analytics and Data Science; Finance; Performance

January 2014 (Revised December 2014)
Case

GenapSys: Business Models for the Genome

By: Richard G. Hamermesh, Joseph B. Fuller and Matthew Preble

GenapSys, a California-based startup, was soon to release a new DNA sequencer that the company's founder, Hesaam Esfandyarpour, believed was truly revolutionary. The sequencer would be substantially less expensive—potentially costing just a few thousand dollars—and... View Details

Keywords: DNA Sequencing; Life Sciences; Business Model; Innovation ＆ Entrepreneurship; Health Care and Treatment; Genetics; Business Strategy; Biotechnology Industry; Pharmaceutical Industry; Technology Industry; Health Industry; Medical Devices and Supplies Industry; United States

2024
Working Paper

Scaling Core Earnings Measurement with Large Language Models

By: Matthew Shaffer and Charles CY Wang

We study the application of large language models (LLMs) to the estimation of core earnings, i.e., a firm's persistent profitability from its core business activities. This construct is central to investors' assessments of economic performance and valuations. However,... View Details

Keywords: Large Language Models; AI and Machine Learning; Accounting; Profit; Corporate Disclosure; Analytics and Data Science; Measurement and Metrics

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