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(159)
- News (42)
- Research (93)
- Multimedia (5)
- Faculty Publications (73)
Show Results For
- All HBS Web
(159)
- News (42)
- Research (93)
- Multimedia (5)
- Faculty Publications (73)
- 2023
- Working Paper
Digital Lending and Financial Well-Being: Through the Lens of Mobile Phone Data
By: AJ Chen, Omri Even-Tov, Jung Koo Kang and Regina Wittenberg-Moerman
To mitigate information asymmetry about borrowers in developing economies, digital lenders utilize machine-learning algorithms and nontraditional data from borrowers’ mobile devices. Consequently, digital lenders have managed to expand access to credit for millions of... View Details
Keywords: Borrowing and Debt; Credit; AI and Machine Learning; Welfare; Well-being; Developing Countries and Economies; Equality and Inequality
Chen, AJ, Omri Even-Tov, Jung Koo Kang, and Regina Wittenberg-Moerman. "Digital Lending and Financial Well-Being: Through the Lens of Mobile Phone Data." Harvard Business School Working Paper, No. 23-076, April 2023. (Revised November 2023. SSRN Working Paper Series, November 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.
- 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
Johnson, Matthew S., David I. Levine, and Michael W. Toffel. "Making Workplaces Safer Through Machine Learning." Regulatory Review (February 26, 2024).
- Research Summary
Overview
By: Ayelet Israeli
Professor Israeli utilizes econometric methods and field experiments to study data driven decision making in marketing context. Her research focuses on data-driven marketing, with an emphasis on how businesses can leverage their own data, customer data, and market data... View Details
- February 2024
- Teaching Note
Data-Driven Denim: Financial Forecasting at Levi Strauss
By: Mark Egan
Teaching Note for HBS Case No. 224-029. Levi Strauss & Co. (“Levi Strauss”) partnered with the IT services company Wipro to incorporate more sophisticated methods, such as machine learning, into their financial forecasting process starting in 2018. The decision to... View Details
- July 2023
- Case
DayTwo: Going to Market with Gut Microbiome (Abridged)
By: Ayelet Israeli
DayTwo is a young Israeli startup that applies research on the gut microbiome and machine learning algorithms to deliver personalized nutritional recommendations to its users in order to minimize blood sugar spikes after meals. After a first year of trial rollout in... View Details
Keywords: Business Startups; AI and Machine Learning; Nutrition; Market Entry and Exit; Product Marketing; Distribution Channels
Israeli, Ayelet. "DayTwo: Going to Market with Gut Microbiome (Abridged)." Harvard Business School Case 524-015, July 2023.
- 2023
- Working Paper
Insufficiently Justified Disparate Impact: A New Criterion for Subgroup Fairness
By: Neil Menghani, Edward McFowland III and Daniel B. Neill
In this paper, we develop a new criterion, "insufficiently justified disparate impact" (IJDI), for assessing whether recommendations (binarized predictions) made by an algorithmic decision support tool are fair. Our novel, utility-based IJDI criterion evaluates false... View Details
Menghani, Neil, Edward McFowland III, and Daniel B. Neill. "Insufficiently Justified Disparate Impact: A New Criterion for Subgroup Fairness." Working Paper, June 2023.
- 2024
- Article
Neyman Meets Causal Machine Learning: Experimental Evaluation of Individualized Treatment Rules
By: Michael Lingzhi Li and Kosuke Imai
A century ago, Neyman showed how to evaluate the efficacy of treatment using a randomized experiment under a minimal set of assumptions. This classical repeated sampling framework serves as a basis of routine experimental analyses conducted by today’s scientists across... View Details
Li, Michael Lingzhi, and Kosuke Imai. "Neyman Meets Causal Machine Learning: Experimental Evaluation of Individualized Treatment Rules." Journal of Causal Inference 12, no. 1 (2024).
- Research Summary
Overview
By: Roberto Verganti
Roberto’s research focuses on how to create innovations that are meaningful for people, for society, and for their creators. He explores how leaders and organizations generate radically new visions, and make those visions come real. His studies lie at the intersection... View Details
- Web
Required Curriculum - MBA
to critically evaluate data science methodologies, results, and recommendations. Fluency in the vocabulary and logic used by data scientists to drive key organizational decisions. Exposure to emerging technologies like generative AI and... View Details
- 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).
- Web
Marketing - Faculty & Research
Marketing Overview Faculty Curriculum Seminars & Conferences Awards & Honors Doctoral Students Featured Publication Frontiers: Can an AI Algorithm Mitigate Racial Economic Inequality? An Analysis in the... View Details
- 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.
- 19 Feb 2019
- First Look
New Research and Ideas, February 19, 2019
algorithm uncovers two distinct behavioral types: "leaders" and "managers." Leaders focus on multi-function, high-level meetings, while managers focus on one-to-one meetings with core functions. Firms with leader CEOs are on average more... View Details
Keywords: Sean Silverthorne
- June 2020
- Article
Real-time Data from Mobile Platforms to Evaluate Sustainable Transportation Infrastructure
By: Omar Isaac Asensio, Kevin Alvarez, Arielle Dror, Emerson Wenzel, Catharina Hollauer and Sooji Ha
By displacing gasoline and diesel fuels, electric cars and fleets reduce emissions from the transportation sector, thus offering important public health benefits. However, public confidence in the reliability of charging infrastructure remains a fundamental barrier to... View Details
Keywords: Environmental Sustainability; Transportation; Infrastructure; Behavior; AI and Machine Learning; Demand and Consumers
Asensio, Omar Isaac, Kevin Alvarez, Arielle Dror, Emerson Wenzel, Catharina Hollauer, and Sooji Ha. "Real-time Data from Mobile Platforms to Evaluate Sustainable Transportation Infrastructure." Nature Sustainability 3, no. 6 (June 2020): 463–471.
- 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).
- 19 Dec 2023
- Research & Ideas
The 10 Most Popular Articles of 2023
life that includes rest, relationships, and a rewarding career. Is AI Coming for Your Job?In a post-AI world, where an algorithm can draft marketing copy—or even pop songs and movie scripts—anything seems... View Details
Keywords: by Danielle Kost
- 01 Dec 2023
- News
Thinking Ahead
As we wind down 2023, there’s talk everywhere of generative AI and how it will fundamentally alter the world as we know it; but how does that translate for your corner of the business world? Is TikTok something you need to take seriously? (Is it time to dance?) We... View Details
- 19 Jan 2023
- Research & Ideas
What Makes Employees Trust (vs. Second-Guess) AI?
industry now. AI improves human decision-making The research emerges as LISH joins the newly launched Digital, Data, and Design Institute at Harvard. The 12-lab organization launched last year to study six themes including View Details
Keywords: by Rachel Layne
- October–December 2022
- Article
Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem
By: Mochen Yang, Edward McFowland III, Gordon Burtch and Gediminas Adomavicius
Combining machine learning with econometric analysis is becoming increasingly prevalent in both research and practice. A common empirical strategy involves the application of predictive modeling techniques to "mine" variables of interest from available data, followed... View Details
Keywords: Machine Learning; Econometric Analysis; Instrumental Variable; Random Forest; Causal Inference; AI and Machine Learning; Forecasting and Prediction
Yang, Mochen, Edward McFowland III, Gordon Burtch, and Gediminas Adomavicius. "Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem." INFORMS Journal on Data Science 1, no. 2 (October–December 2022): 138–155.