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- Faculty Publications (263)
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- 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).
- 2023
- Working Paper
The Uneven Impact of Generative AI on Entrepreneurial Performance
By: Nicholas G. Otis, Rowan Clarke, Solène Delecourt, David Holtz and Rembrand Koning
There is a growing belief that scalable and low-cost AI assistance can improve firm
decision-making and economic performance. However, running a business involves
a myriad of open-ended problems, making it hard to generalize from recent studies
showing that...
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Keywords:
AI and Machine Learning;
Performance Improvement;
Small Business;
Decision Choices and Conditions;
Kenya
Otis, Nicholas G., Rowan Clarke, Solène Delecourt, David Holtz, and Rembrand Koning. "The Uneven Impact of Generative AI on Entrepreneurial Performance." Harvard Business School Working Paper, No. 24-042, December 2023.
- 2023
- Article
Verifiable Feature Attributions: A Bridge between Post Hoc Explainability and Inherent Interpretability
By: Usha Bhalla, Suraj Srinivas and Himabindu Lakkaraju
With the increased deployment of machine learning models in various real-world applications, researchers and practitioners alike have emphasized the need for explanations of model behaviour. To this end, two broad strategies have been outlined in prior literature to...
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Bhalla, Usha, Suraj Srinivas, and Himabindu Lakkaraju. "Verifiable Feature Attributions: A Bridge between Post Hoc Explainability and Inherent Interpretability." Advances in Neural Information Processing Systems (NeurIPS) (2023).
- 2023
- Article
Which Models Have Perceptually-Aligned Gradients? An Explanation via Off-Manifold Robustness
By: Suraj Srinivas, Sebastian Bordt and Himabindu Lakkaraju
One of the remarkable properties of robust computer vision models is that their input-gradients are often aligned with human perception, referred to in the literature as perceptually-aligned gradients (PAGs). Despite only being trained for classification, PAGs cause...
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Srinivas, Suraj, Sebastian Bordt, and Himabindu Lakkaraju. "Which Models Have Perceptually-Aligned Gradients? An Explanation via Off-Manifold Robustness." Advances in Neural Information Processing Systems (NeurIPS) (2023).
- November 2023
- Case
Riiid: Scaling AI Educational Services Globally
By: John Jong-Hyun Kim, Nancy Dai and Ruru Hoong
This article explores the definition and evolution of AI, its applications in education, and the role of AI, particularly in K-12 education. It discusses the founding of Riiid, an AI-driven educational technology company, and its journey in the education sector, with a...
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Keywords:
AI and Machine Learning;
Economic Sectors;
Technological Innovation;
Education Industry;
South Korea;
Asia
Kim, John Jong-Hyun, Nancy Dai, and Ruru Hoong. "Riiid: Scaling AI Educational Services Globally." Harvard Business School Case 324-030, November 2023.
- November 2023
- Case
Copilot(s): Generative AI at Microsoft and GitHub
This case tells the story of Microsoft’s 2018 acquisition of GitHub and the subsequent launch of GitHub Copilot, a tool that uses generative artificial intelligence to suggest snippets of code to software developers in real time. Set in late 2021, when Copilot was...
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Keywords:
Business Ventures;
Strategy;
AI and Machine Learning;
Applications and Software;
Product Launch;
Information Technology Industry;
Technology Industry;
Web Services Industry;
United States;
California
Nagle, Frank, Shane Greenstein, Maria P. Roche, Nataliya Langburd Wright, and Sarah Mehta. "Copilot(s): Generative AI at Microsoft and GitHub." Harvard Business School Case 624-010, November 2023.
- November 2023 (Revised April 2024)
- Case
Khanmigo: Revolutionizing Learning with GenAI
By: William A. Sahlman, Allison M. Ciechanover and Emily Grandjean
Already a leader in the edtech space since its 2008 launch, Khan Academy was now one of the first edtech organizations to embrace generative artificial intelligence ("genAI"). In March 2023, Khan Academy began beta testing Khanmigo, a genAI “guide” and tutor built with...
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Keywords:
Technology Adoption;
Leading Change;
Entrepreneurship;
Risk and Uncertainty;
Education Industry;
Technology Industry;
United States;
San Francisco
Sahlman, William A., Allison M. Ciechanover, and Emily Grandjean. "Khanmigo: Revolutionizing Learning with GenAI." Harvard Business School Case 824-059, November 2023. (Revised April 2024.)
- Working Paper
An AI Method to Score Celebrity Visual Potential from Human Faces
By: Flora Feng, Shunyuan Zhang, Xiao Liu, Kannan Srinivasan and Cait Lamberton
Celebrities have extraordinary abilities to attract and influence others. Predicting celebrity visual potential is important in the domains of business, politics, media, and entertainment. Can we use human faces to predict celebrity visual potential? If so, which...
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Feng, Flora, Shunyuan Zhang, Xiao Liu, Kannan Srinivasan, and Cait Lamberton. "An AI Method to Score Celebrity Visual Potential from Human Faces." SSRN Working Paper Series, No. 4071188, November 2023.
- November–December 2023
- Article
Keep Your AI Projects on Track
By: Iavor Bojinov
AI—and especially its newest star, generative AI—is today a central theme in corporate boardrooms, leadership discussions, and casual exchanges among employees eager to supercharge their productivity. Sadly, beneath the aspirational headlines and tantalizing potential...
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Keywords:
Generative Models;
AI and Machine Learning;
Success;
Failure;
Product Development;
Technology Adoption
Bojinov, Iavor. "Keep Your AI Projects on Track." Harvard Business Review 101, no. 6 (November–December 2023): 53–59.
- November 2023
- Article
Psychological Factors Underlying Attitudes toward AI Tools
By: Julian De Freitas, Stuti Agarwal, B. Schmitt and N. Haslam
What are the psychological factors driving attitudes toward AI tools, and how can resistance to AI systems be overcome when they are beneficial? In this perspective, we first organize the main sources of resistance into five main categories: opacity, emotionlessness,...
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De Freitas, Julian, Stuti Agarwal, B. Schmitt, and N. Haslam. "Psychological Factors Underlying Attitudes toward AI Tools." Nature Human Behaviour 7, no. 11 (November 2023): 1845–1854.
- 2023
- Working Paper
The Optimal Stock Valuation Ratio
By: Sebastian Hillenbrand and Odhrain McCarthy
Trailing price ratios, such as the price-dividend and the price-earnings ratio, scale prices by trailing cash flow measures. They theoretically contain expected returns, yet, their performance in predicting stock market returns is poor. This is because of an omitted...
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Keywords:
Price;
Investment Return;
AI and Machine Learning;
Valuation;
Cash Flow;
Forecasting and Prediction
Hillenbrand, Sebastian, and Odhrain McCarthy. "The Optimal Stock Valuation Ratio." Working Paper, November 2023.
- October 2023
- Teaching Note
Timnit Gebru: 'SILENCED No More' on AI Bias and The Harms of Large Language Models
By: Tsedal Neeley and Tim Englehart
Teaching Note for HBS Case No. 422-085. Dr. Timnit Gebru—a leading artificial intelligence (AI) computer scientist and co-lead of Google’s Ethical AI team—was messaging with one of her colleagues when she saw the words: “Did you resign?? Megan sent an email saying that...
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- October 2023
- Case
ReUp Education: Can AI Help Learners Return to College?
By: Kris Ferreira, Christopher Thomas Ryan and Sarah Mehta
Founded in 2015, ReUp Education helps “stopped out students”—learners who have stopped making progress towards graduation—achieve their college completion goals. The company relies on a team of success coaches to engage with learners and help them reenroll. In 2019,...
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Keywords:
AI;
Algorithms;
Machine Learning;
Edtech;
Education Technology;
Analysis;
Higher Education;
AI and Machine Learning;
Customization and Personalization;
Failure;
Education Industry;
Technology Industry;
United States
Ferreira, Kris, Christopher Thomas Ryan, and Sarah Mehta. "ReUp Education: Can AI Help Learners Return to College?" Harvard Business School Case 624-007, October 2023.
- October 2023
- Case
Fixie and Conversational AI Sidekicks
By: Jeffrey J. Bussgang and Carin-Isabel Knoop
In March 2023, Fixie Co-Founder and Chief Architect Matt Welsh and co-founders had the kind of meeting no founders want to have. The president of leading artificial intelligence (AI) research and deployment firm OpenAI, which had catapulted into fame with its ChatGPT...
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Keywords:
Large Language Model;
Entrepreneurship;
Decision Choices and Conditions;
AI and Machine Learning;
Technological Innovation;
Competitive Strategy;
Technology Industry;
United States
Bussgang, Jeffrey J., and Carin-Isabel Knoop. "Fixie and Conversational AI Sidekicks." Harvard Business School Case 824-037, October 2023.
- October 2023 (Revised February 2024)
- Case
Loris
By: Shunyuan Zhang, Das Narayandas, Stacy Straaberg and David Lane
In December 2022, Loris’s executive team considered their go-to-market strategy. Loris was an artificial intelligence (AI) software startup for the customer service industry with two products on the market: 1) Agent Assist which provided customer service agents (CSAs)...
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- 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.
- 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...
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Pawelczyk, Martin, Seth Neel, and Himabindu Lakkaraju. "In-Context Unlearning: Language Models as Few Shot Unlearners." Working Paper, October 2023.
- 2023
- Working Paper
The Impact of Input Inaccuracy on Leveraging AI Tools: Evidence from Algorithmic Labor Scheduling
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...
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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.
- October 14, 2023
- Article
Will Consumers Buy Selfish Self-Driving Cars?
De Freitas, Julian. "Will Consumers Buy Selfish Self-Driving Cars?" Wall Street Journal (October 14, 2023), C5.
- September 2023 (Revised September 2023)
- Teaching Note
Accelerating AI Adoption in the U.S. Air Force
By: Maria P. Roche
Teaching Note for HBS Case No. 723-429.
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Keywords:
AI;
"USA,";
Military;
Strategy;
Innovation;
Economic Growth;
Organizational Design;
Technology