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- January 2025
- Case
PayJoy: Finance for the Next Billion
By: Boris Groysberg and Sarah L. Abbott
PayJoy, an impact-driven financial technology company founded in 2015, provides smartphone financing and other financial products to customers who lack access to traditional credit products. As of early 2025, PayJoy had issued $2.5 billion in loans to 13 million... View Details
Keywords: Social Impact; Fintech; Developing Countries; Underbanked; Algorithm; Credit; South America; Mission And Purpose; Start-up; Entrepreneurship; Growth; Data Analysis; Technology; Business Ventures; Business Startups; Business Model; Growth and Development; Information Technology; Social Enterprise; Financial Services Industry; South America; South Africa; Asia
- 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
Imai, Kosuke, and Michael Lingzhi Li. "Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments." Journal of Business & Economic Statistics 43, no. 1 (2025): 256–268.
- November 2024 (Revised January 2025)
- Case
MiDAS: Automating Unemployment Benefits
By: Shikhar Ghosh and Shweta Bagai
In 2015, the state of Michigan considered whether to nominate its Michigan Integrated Data Automated System (MiDAS) for a prestigious state technology award. Launched in 2013 amid severe budget pressures, the $47 million automated fraud detection system was designed to... View Details
Keywords: Artificial Intelligence; AI; Machine Learning Models; Algorithmic Data; Decision Making; Automation; Benefits; Compensation; Cost Reduction; Digital Transformation; Employment; Government; Fraud; Government Technology; Public Sector; Systems; Systems Integration; Unemployment Insurance; Waste Heat Recovery; AI and Machine Learning; Government Administration; Information Technology; Insurance; United States
- November–December 2024
- Article
Outcome-Driven Dynamic Refugee Assignment with Allocation Balancing
By: Kirk Bansak and Elisabeth Paulson
This study proposes two new dynamic assignment algorithms to match refugees and asylum seekers to geographic localities within a host country. The first, currently implemented in a multi-year pilot in Switzerland, seeks to maximize the average predicted employment... View Details
Bansak, Kirk, and Elisabeth Paulson. "Outcome-Driven Dynamic Refugee Assignment with Allocation Balancing." Operations Research 72, no. 6 (November–December 2024): 2375–2390.
- 2024
- Article
Learning Under Random Distributional Shifts
By: Kirk Bansak, Elisabeth Paulson and Dominik Rothenhäusler
Algorithmic assignment of refugees and asylum seekers to locations within host
countries has gained attention in recent years, with implementations in the U.S.
and Switzerland. These approaches use data on past arrivals to generate machine
learning models that can... View Details
Bansak, Kirk, Elisabeth Paulson, and Dominik Rothenhäusler. "Learning Under Random Distributional Shifts." Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS) 27th (2024).
- 2024
- Book
Fintech, Small Business & the American Dream: How Technology Is Transforming Lending and Shaping a New Era of Small Business Opportunity
By: Karen G. Mills
The second edition of Fintech, Small Business & the American Dream, builds on the groundbreaking 2019 book with new insights on how technology and artificial intelligence are transforming small business lending. This ambitious view covers the significance of... View Details
Keywords: Fintech; AI; AI and Machine Learning; Small Business; Economy; Technology Adoption; Credit; Financing and Loans; Analytics and Data Science
Mills, Karen G. Fintech, Small Business & the American Dream: How Technology Is Transforming Lending and Shaping a New Era of Small Business Opportunity. 2nd Edition, NY: Palgrave Macmillan, 2024.
- April 2024
- Article
A Machine Learning Algorithm Predicting Risk of Dilating VUR among Infants with Hydronephrosis Using UTD Classification
By: Hsin-Hsiao Scott Wang, Michael Lingzhi Li, Dylan Cahill, John Panagides, Tanya Logvinenko, Jeanne Chow and Caleb Nelson
Backgrounds: Urinary Tract Dilation (UTD) classification has been designed to be a more objective grading system to evaluate antenatal and post-natal UTD. Due to unclear association between UTD classifications to specific anomalies such as vesico-ureteral reflux (VUR),... View Details
Wang, Hsin-Hsiao Scott, Michael Lingzhi Li, Dylan Cahill, John Panagides, Tanya Logvinenko, Jeanne Chow, and Caleb Nelson. "A Machine Learning Algorithm Predicting Risk of Dilating VUR among Infants with Hydronephrosis Using UTD Classification." Journal of Pediatric Urology 20, no. 2 (April 2024): 271–278.
- April 2024
- Article
Decision Authority and the Returns to Algorithms
By: Hyunjin Kim, Edward L. Glaeser, Andrew Hillis, Scott Duke Kominers and Michael Luca
We evaluate a pilot in an Inspections Department to explore the returns to a pair of algorithms that varied in their sophistication. We find that both algorithms provided substantial prediction gains, suggesting that even simple data may be helpful. However, these... View Details
Keywords: Algorithmic Aversion; Algorithmic Decision Making; Algorithms; Public Entrepreneurship; Govenment; Local Government; Crowdsourcing; Crowdsourcing Contests; Inspection; Principal-agent Theory; Government Administration; Decision Making; Public Administration Industry; United States
Kim, Hyunjin, Edward L. Glaeser, Andrew Hillis, Scott Duke Kominers, and Michael Luca. "Decision Authority and the Returns to Algorithms." Strategic Management Journal 45, no. 4 (April 2024): 619–648.
- 2024
- Working Paper
The Cram Method for Efficient Simultaneous Learning and Evaluation
By: Zeyang Jia, Kosuke Imai and Michael Lingzhi Li
We introduce the "cram" method, a general and efficient approach to simultaneous learning and evaluation using a generic machine learning (ML) algorithm. In a single pass of batched data, the proposed method repeatedly trains an ML algorithm and tests its empirical... View Details
Keywords: AI and Machine Learning
Jia, Zeyang, Kosuke Imai, and Michael Lingzhi Li. "The Cram Method for Efficient Simultaneous Learning and Evaluation." Working Paper, March 2024.
- 2023
- Working Paper
An Experimental Design for Anytime-Valid Causal Inference on Multi-Armed Bandits
By: Biyonka Liang and Iavor I. Bojinov
Typically, multi-armed bandit (MAB) experiments are analyzed at the end of the study and thus require the analyst to specify a fixed sample size in advance. However, in many online learning applications, it is advantageous to continuously produce inference on the... View Details
Liang, Biyonka, and Iavor I. Bojinov. "An Experimental Design for Anytime-Valid Causal Inference on Multi-Armed Bandits." Harvard Business School Working Paper, No. 24-057, March 2024.
- March 2024
- Case
Unintended Consequences of Algorithmic Personalization
By: Eva Ascarza and Ayelet Israeli
“Unintended Consequences of Algorithmic Personalization” (HBS No. 524-052) investigates algorithmic bias in marketing through four case studies featuring Apple, Uber, Facebook, and Amazon. Each study presents scenarios where these companies faced public criticism for... View Details
Keywords: Race; Gender; Marketing; Diversity; Customer Relationship Management; Prejudice and Bias; Customization and Personalization; Technology Industry; Retail Industry; United States
Ascarza, Eva, and Ayelet Israeli. "Unintended Consequences of Algorithmic Personalization." Harvard Business School Case 524-052, March 2024.
- February 2024
- Module Note
Data-Driven Marketing in Retail Markets
By: Ayelet Israeli
This note describes an eight-class sessions module on data-driven marketing in retail markets. The module aims to familiarize students with core concepts of data-driven marketing in retail, including exploring the opportunities and challenges, adopting best practices,... View Details
Keywords: Data; Data Analytics; Retail; Retail Analytics; Data Science; Business Analytics; "Marketing Analytics"; Omnichannel; Omnichannel Retailing; Omnichannel Retail; DTC; Direct To Consumer Marketing; Ethical Decision Making; Algorithmic Bias; Privacy; A/B Testing; Descriptive Analytics; Prescriptive Analytics; Predictive Analytics; Analytics and Data Science; E-commerce; Marketing Channels; Demand and Consumers; Marketing Strategy; Retail Industry
Israeli, Ayelet. "Data-Driven Marketing in Retail Markets." Harvard Business School Module Note 524-062, February 2024.
- 2024
- Working Paper
Warnings and Endorsements: Improving Human-AI Collaboration Under Covariate Shift
By: Matthew DosSantos DiSorbo and Kris Ferreira
Problem definition: While artificial intelligence (AI) algorithms may perform well on data that are representative of the training set (inliers), they may err when extrapolating on non-representative data (outliers). These outliers often originate from covariate shift,... View Details
DosSantos DiSorbo, Matthew, and Kris Ferreira. "Warnings and Endorsements: Improving Human-AI Collaboration Under Covariate Shift." Working Paper, February 2024.
- January 2024 (Revised February 2024)
- Course Overview Note
Managing Customers for Growth: Course Overview for Students
By: Eva Ascarza
Managing Customers for Growth (MCG) is a 14-session elective course for second-year MBA students at Harvard Business School. It is designed for business professionals engaged in roles centered on customer-driven growth activities. The course explores the dynamics of... View Details
Keywords: Customer Relationship Management; Decision Making; Analytics and Data Science; Growth Management; Telecommunications Industry; Technology Industry; Financial Services Industry; Education Industry; Travel Industry
Ascarza, Eva. "Managing Customers for Growth: Course Overview for Students." Harvard Business School Course Overview Note 524-032, January 2024. (Revised February 2024.)
- December 2023
- Case
TikTok: The Algorithm Will See You Now
By: Shikhar Ghosh and Shweta Bagai
In a world where attention is a scarce commodity, this case explores the meteoric rise of TikTok—an app that transformed from a niche platform for teens into the most visited domain by 2021—surpassing even Google. Its algorithm was a sophisticated mechanism for... View Details
Keywords: Social Media; Applications and Software; Disruptive Innovation; Business and Government Relations; International Relations; Cybersecurity; Culture; Technology Industry; China; United States; India
Ghosh, Shikhar, and Shweta Bagai. "TikTok: The Algorithm Will See You Now." Harvard Business School Case 824-125, December 2023.
- 2023
- Article
Benchmarking Large Language Models on CMExam—A Comprehensive Chinese Medical Exam Dataset
By: Junling Liu, Peilin Zhou, Yining Hua, Dading Chong, Zhongyu Tian, Andrew Liu, Helin Wang, Chenyu You, Zhenhua Guo, Lei Zhu and Michael Lingzhi Li
Recent advancements in large language models (LLMs) have transformed the field of question answering (QA). However, evaluating LLMs in the medical field is challenging due to the lack of standardized and comprehensive datasets. To address this gap, we introduce CMExam,... View Details
Keywords: Large Language Model; AI and Machine Learning; Analytics and Data Science; Health Industry
Liu, Junling, Peilin Zhou, Yining Hua, Dading Chong, Zhongyu Tian, Andrew Liu, Helin Wang, Chenyu You, Zhenhua Guo, Lei Zhu, and Michael Lingzhi Li. "Benchmarking Large Language Models on CMExam—A Comprehensive Chinese Medical Exam Dataset." Conference on Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track 36 (2023).
- 2023
- Working Paper
In-Context Unlearning: Language Models as Few Shot Unlearners
By: Martin Pawelczyk, Seth Neel and Himabindu Lakkaraju
Machine unlearning, the study of efficiently removing the impact of specific training points on the
trained model, has garnered increased attention of late, driven by the need to comply with privacy
regulations like the Right to be Forgotten. Although unlearning is... View Details
Pawelczyk, Martin, Seth Neel, and Himabindu Lakkaraju. "In-Context Unlearning: Language Models as Few Shot Unlearners." Working Paper, October 2023.
- October 2023
- Article
Matching Mechanisms for Refugee Resettlement
By: David Delacrétaz, Scott Duke Kominers and Alexander Teytelboym
Current refugee resettlement processes account for neither the preferences of refugees nor the priorities of hosting communities. We introduce a new framework for matching with multidimensional knapsack constraints that captures the (possibly multidimensional) sizes of... View Details
Keywords: Refugee Resettlement; Matching; Matching Markets; Matching Platform; Matching With Contracts; Algorithms; Refugees; Market Design
Delacrétaz, David, Scott Duke Kominers, and Alexander Teytelboym. "Matching Mechanisms for Refugee Resettlement." American Economic Review 113, no. 10 (October 2023): 2689–2717.
- 2023
- Working Paper
The Impact of Input Inaccuracy on Leveraging AI Tools: Evidence from Algorithmic Labor Scheduling
By: Caleb Kwon, Antonio Moreno and Ananth Raman
Are the inputs used by your AI tool correct and up to date? In this paper, we show that the answer to this question: (i) is frequently a “no” in real business contexts, and (ii) has significant implications on the performance of AI tools. In the context of algorithmic... View Details
Kwon, Caleb, Antonio Moreno, and Ananth Raman. "The Impact of Input Inaccuracy on Leveraging AI Tools: Evidence from Algorithmic Labor Scheduling." Working Paper, October 2023.
- September–October 2023
- Article
Interpretable Matrix Completion: A Discrete Optimization Approach
By: Dimitris Bertsimas and Michael Lingzhi Li
We consider the problem of matrix completion on an n × m matrix. We introduce the problem of interpretable matrix completion that aims to provide meaningful insights for the low-rank matrix using side information. We show that the problem can be... View Details
Keywords: Mathematical Methods
Bertsimas, Dimitris, and Michael Lingzhi Li. "Interpretable Matrix Completion: A Discrete Optimization Approach." INFORMS Journal on Computing 35, no. 5 (September–October 2023): 952–965.