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Show Results For
-
All HBS Web
(682)
- News (167)
- Research (289)
- Events (4)
- Multimedia (6)
- Faculty Publications (216)
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- 2023
- Working Paper
The Customer Journey as a Source of Information
By: Nicolas Padilla, Eva Ascarza and Oded Netzer
In the face of heightened data privacy concerns and diminishing third-party data access,
firms are placing increased emphasis on first-party data (1PD) for marketing decisions.
However, in environments with infrequent purchases, reliance on past purchases 1PD...
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Keywords:
Customer Journey;
Privacy;
Consumer Behavior;
Analytics and Data Science;
AI and Machine Learning;
Customer Focus and Relationships
Padilla, Nicolas, Eva Ascarza, and Oded Netzer. "The Customer Journey as a Source of Information." Harvard Business School Working Paper, No. 24-035, October 2023. (Revised October 2023.)
- February 2024
- Case
Continuity & Change at Boston Consulting Group
By: David G. Fubini, Suraj Srinivasan and David Lane
As the new CEO of Boston Consulting Group (BCG) since autumn 2021, Christoph Schweizer had big shoes to fill—his predecessor, Rich Lesser, had tripled the partnership’s total revenues and created digital initiatives that contributed 40+% of 2021 revenues, more than...
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Keywords:
Business Organization;
Change Management;
Talent and Talent Management;
Governance;
AI and Machine Learning;
Environmental Sustainability;
Leading Change;
Risk Management;
Organizational Culture;
Partners and Partnerships;
Revenue;
Growth and Development Strategy;
Management Succession;
Consulting Industry
Fubini, David G., Suraj Srinivasan, and David Lane. "Continuity & Change at Boston Consulting Group." Harvard Business School Case 124-011, February 2024.
- Working Paper
Visual Uniqueness in Peer-to-Peer Marketplaces: Machine Learning Model Development, Validation, and Application
By: Flora Feng, Charis Li and Shunyuan Zhang
Peer-to-peer (P2P) marketplaces have seen exponential growth in recent years featured by unique offerings from individual providers. Despite the perceived value of uniqueness, scalable quantification of visual uniqueness in P2P platforms like Airbnb has been largely...
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Keywords:
Peer-to-peer Markets;
Marketplace Matching;
AI and Machine Learning;
Demand and Consumers;
Digital Platforms;
Marketing
Feng, Flora, Charis Li, and Shunyuan Zhang. "Visual Uniqueness in Peer-to-Peer Marketplaces: Machine Learning Model Development, Validation, and Application." SSRN Working Paper Series, No. 4665286, February 2024.
- 2019
- 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
Fintech, Small Business & the American Dream describes the needs of small businesses for capital and demonstrates how technology—novel data sources, artificial intelligence, machine learning—will transform the small business lending market. This market has been...
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Keywords:
Fintech;
Big Data;
Data;
Technology;
Artificial Intelligence;
Great Recession;
Regulation;
Innovation;
Banks;
Lending;
Loans;
Access To Capital;
American Dream;
Community Banking;
Small Business Administration;
Entrepreneur;
Government;
Public Policy;
API;
Policy Making;
Small Business;
Financing and Loans;
Technological Innovation;
Financial Crisis;
Banks and Banking;
Governing Rules, Regulations, and Reforms;
Policy;
AI and Machine Learning;
Analytics and Data Science;
United States
Mills, Karen G. Fintech, Small Business & the American Dream: How Technology Is Transforming Lending and Shaping a New Era of Small Business Opportunity. Palgrave Macmillan, 2019.
- 2023
- Article
Towards Bridging the Gaps between the Right to Explanation and the Right to Be Forgotten
By: Himabindu Lakkaraju, Satyapriya Krishna and Jiaqi Ma
The Right to Explanation and the Right to be Forgotten are two important principles outlined to regulate algorithmic decision making and data usage in real-world applications. While the right to explanation allows individuals to request an actionable explanation for an...
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Keywords:
Analytics and Data Science;
AI and Machine Learning;
Decision Making;
Governing Rules, Regulations, and Reforms
Lakkaraju, Himabindu, Satyapriya Krishna, and Jiaqi Ma. "Towards Bridging the Gaps between the Right to Explanation and the Right to Be Forgotten." Proceedings of the International Conference on Machine Learning (ICML) 40th (2023): 17808–17826.
- 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).
- 2022
- Working Paper
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...
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Bansak, Kirk, and Elisabeth Paulson. "Outcome-Driven Dynamic Refugee Assignment with Allocation Balancing." Harvard Business School Working Paper, No. 23-048, January 2022.
- March 16, 2021
- Article
From Driverless Dilemmas to More Practical Commonsense Tests for Automated Vehicles
By: Julian De Freitas, Andrea Censi, Bryant Walker Smith, Luigi Di Lillo, Sam E. Anthony and Emilio Frazzoli
For the first time in history, automated vehicles (AVs) are being deployed in populated environments. This unprecedented transformation of our everyday lives demands a significant undertaking: endowing
complex autonomous systems with ethically acceptable behavior. We...
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Keywords:
Automated Driving;
Public Health;
Artificial Intelligence;
Transportation;
Health;
Ethics;
Policy;
AI and Machine Learning
De Freitas, Julian, Andrea Censi, Bryant Walker Smith, Luigi Di Lillo, Sam E. Anthony, and Emilio Frazzoli. "From Driverless Dilemmas to More Practical Commonsense Tests for Automated Vehicles." Proceedings of the National Academy of Sciences 118, no. 11 (March 16, 2021).
- 19 Mar 2019
- First Look
New Research and Ideas, March 19, 2019
forthcoming Academy of Management Discoveries Creativity, Artificial Intelligence, and a World of Surprises By: Amabile, Teresa M. Abstract—In recent years, progress has been made toward AI Creativity, which I define as the production of...
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Keywords:
Dina Gerdeman
- 2023
- Other Article
The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applications
By: Mirac Suzgun, Luke Melas-Kyriazi, Suproteem K. Sarkar, Scott Duke Kominers and Stuart Shieber
Innovation is a major driver of economic and social development, and information about many kinds of innovation is embedded in semi-structured data from patents and patent applications. Though the impact and novelty of innovations expressed in patent data are difficult...
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Keywords:
USPTO;
Natural Language Processing;
Classification;
Summarization;
Patent Novelty;
Patent Trolls;
Patent Enforceability;
Patents;
Innovation and Invention;
Intellectual Property;
AI and Machine Learning;
Analytics and Data Science
Suzgun, Mirac, Luke Melas-Kyriazi, Suproteem K. Sarkar, Scott Duke Kominers, and Stuart Shieber. "The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applications." Conference on Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track 36 (2023).
- 2022
- Working Paper
Rethinking Explainability as a Dialogue: A Practitioner's Perspective
By: Himabindu Lakkaraju, Dylan Slack, Yuxin Chen, Chenhao Tan and Sameer Singh
As practitioners increasingly deploy machine learning models in critical domains such as healthcare, finance, and policy, it becomes vital to ensure that domain experts function effectively alongside these models. Explainability is one way to bridge the gap between...
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Keywords:
Natural Language Conversations;
AI and Machine Learning;
Experience and Expertise;
Interactive Communication;
Business and Stakeholder Relations
Lakkaraju, Himabindu, Dylan Slack, Yuxin Chen, Chenhao Tan, and Sameer Singh. "Rethinking Explainability as a Dialogue: A Practitioner's Perspective." Working Paper, 2022.
- 23 May 2017
- First Look
First Look at New Ideas and Research: May 23, 2017
patients, and 12% of total Medicare spending are associated with physician beliefs unsupported by clinical evidence. Download working paper: https://www.hbs.edu/faculty/Pages/item.aspx?num=49218 Stock Price Synchronicity and Material...
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Keywords:
Carmen Nobel
- 2024
- Working Paper
Improving Human-Algorithm Collaboration: Causes and Mitigation of Over- and Under-Adherence
By: Maya Balakrishnan, Kris Ferreira and Jordan Tong
Even if algorithms make better predictions than humans on average, humans may sometimes have private information
which an algorithm does not have access to that can improve performance. How can we help humans effectively use
and adjust recommendations made by...
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Keywords:
Cognitive Biases;
Algorithm Transparency;
Forecasting and Prediction;
Behavior;
AI and Machine Learning;
Analytics and Data Science;
Cognition and Thinking
Balakrishnan, Maya, Kris Ferreira, and Jordan Tong. "Improving Human-Algorithm Collaboration: Causes and Mitigation of Over- and Under-Adherence." Working Paper, February 2024.
- 03 Jan 2017
- First Look
January 3, 2017
response to policies that maintain low interest rates, money funds change their product offerings by investing in riskier asset classes, are more likely to exit the market, and reduce the fees they charge their investors. The consequence...
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Keywords:
Carmen Nobel
- 2023
- Working Paper
Black-box Training Data Identification in GANs via Detector Networks
By: Lukman Olagoke, Salil Vadhan and Seth Neel
Since their inception Generative Adversarial Networks (GANs) have been popular generative models across images, audio, video, and tabular data. In this paper we study whether given access to a trained GAN, as well as fresh samples from the underlying distribution, if...
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Olagoke, Lukman, Salil Vadhan, and Seth Neel. "Black-box Training Data Identification in GANs via Detector Networks." Working Paper, October 2023.
- 12 May 2009
- First Look
First Look: May 12, 2009
Management, a "sustainable" investing firm established in 2004 by David Blood and U.S. Vice President AI Gore. Places students in the position of David Lowish, director of global industrials, who...
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Keywords:
Martha Lagace