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Show Results For
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All HBS Web
(164)
- News (41)
- Research (83)
- Multimedia (4)
- Faculty Publications (69)
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- 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...
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- 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...
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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...
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Menghani, Neil, Edward McFowland III, and Daniel B. Neill. "Insufficiently Justified Disparate Impact: A New Criterion for Subgroup Fairness." Working Paper, June 2023.
- 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
- 2023
- Working Paper
Random Distribution Shift in Refugee Placement: Strategies for Building Robust Models
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...
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Bansak, Kirk, Elisabeth Paulson, and Dominik Rothenhäusler. "Random Distribution Shift in Refugee Placement: Strategies for Building Robust Models." Working Paper, June 2023.
- 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...
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Keywords:
Sean Silverthorne
- 2021
- Article
To Thine Own Self Be True? Incentive Problems in Personalized Law
By: Jordan M. Barry, John William Hatfield and Scott Duke Kominers
Recent years have seen an explosion of scholarship on “personalized law.” Commentators foresee a world in which regulators armed with big data and machine learning techniques determine the optimal legal rule for every regulated party, then instantaneously disseminate...
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Keywords:
Personalized Law;
Regulation;
Regulatory Avoidance;
Regulatory Arbitrage;
Law And Economics;
Law And Technology;
Law And Artificial Intelligence;
Futurism;
Moral Hazard;
Elicitation;
Signaling;
Privacy;
Law;
Governing Rules, Regulations, and Reforms;
Information Technology;
AI and Machine Learning
Barry, Jordan M., John William Hatfield, and Scott Duke Kominers. "To Thine Own Self Be True? Incentive Problems in Personalized Law." Art. 2. William & Mary Law Review 62, no. 3 (2021).
- 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,...
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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
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...
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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.
- 03 Jan 2017
- First Look
January 3, 2017
Winter 2017 MIT Sloan Management Review Why Big Data Isn't Enough By: Chai, Sen, and Willy C. Shih Abstract—There is a growing belief that sophisticated algorithms can explore huge databases and find relationships independent of any...
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Keywords:
Carmen Nobel
- 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...
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by Danielle Kost
- 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...
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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.
- 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...
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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.
- 07 Feb 2022
- Research & Ideas
Digital Transformation: A New Roadmap for Success
companies. While they agreed that leaders urgently needed to expand their knowledge, they didn’t see eye to eye on what digital literacy means. A few argued that leaders should understand data analytics and AI deeply, and even learn to...
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- 10 Feb 2020
- In Practice
6 Ways That Emerging Technology Is Disrupting Business Strategy
Economic Research. 3. Algorithms are changing the pricing game “Firms are increasingly using pricing algorithms to set prices, especially in online markets. Pricing View Details
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by Danielle Kost
- 22 Oct 2019
- Research & Ideas
Use Artificial Intelligence to Set Sales Targets That Motivate
handle human tasks—allows companies to use multiple variables to compute the best targets for employees, often in real time. Many companies have started using machine-learning algorithms to construct AI...
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by Michael Blanding
- 16 Dec 2022
- Research & Ideas
Why Technology Alone Can't Solve AI's Bias Problem
human toll to letting algorithms do the work. “Maybe there is a bias from people who have been traditionally hiring men.” Searches on popular recruiting sites might seem like a neutral way to find prospective candidates, but their...
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- 12 Oct 2022
- Research & Ideas
When Design Enables Discrimination: Learning from Anti-Asian Bias on Airbnb
and 334,906 reviews in New York City, one of Airbnb’s major markets. Using reviews as a proxy for bookings, the researchers tracked reviews for the year before and the year after the pandemic began. They turned to NamePrism, a publicly available View Details
- 04 Apr 2022
- Research & Ideas
Tech Hubs: How Software Brought Talent and Prosperity to New Cities
software patents. These classified patents were then used to train a machine learning algorithm to identify among millions of patents those that were software related. Once software and non-software patents were separated, the researchers...
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by Rachel Layne
- 01 Mar 2018
- What Do You Think?
Two Decades Later, is the 'New Economy' Finally Here?
potential for ecommerce growth in the country which relies (on) a lot of machine learning and AI the growth rate through the new economy prospects is just starting to take off here in Bangladesh.” Jacob Navon added, “Is the New Economy...
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Keywords:
by James Heskett