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- Faculty Publications (320)
- September 2023 (Revised January 2024)
- Case
AI21 Labs in 2023: Strategy for Generative AI
By: David Yoffie, Orna Dan and Elena Corsi
Israeli generative artificial intelligence company AI21 Labs was founded in 2017 to realize the vision of true machine intelligence. It sought to reinvent writing and reading and in 2020 it launched Wordtune, an app using GenAI software to offer alternate text...
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Keywords:
Decision Making;
AI and Machine Learning;
Innovation Strategy;
Growth and Development Strategy;
Applications and Software;
Competitive Strategy;
Technology Industry;
Israel
Yoffie, David, Orna Dan, and Elena Corsi. "AI21 Labs in 2023: Strategy for Generative AI." Harvard Business School Case 724-383, September 2023. (Revised January 2024.)
- 2023
- Article
On the Impact of Actionable Explanations on Social Segregation
By: Ruijiang Gao and Himabindu Lakkaraju
As predictive models seep into several real-world applications, it has become critical to ensure that individuals who are negatively impacted by the outcomes of these models are provided with a means for recourse. To this end, there has been a growing body of research...
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Gao, Ruijiang, and Himabindu Lakkaraju. "On the Impact of Actionable Explanations on Social Segregation." Proceedings of the International Conference on Machine Learning (ICML) 40th (2023): 10727–10743.
- September–October 2023
- Article
Reskilling in the Age of AI
In the coming decades, as the pace of technological change continues to increase, millions of workers may need to be not just upskilled but reskilled—a profoundly complex societal challenge that will sometimes require workers to both acquire new skills and...
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Keywords:
Competency and Skills;
AI and Machine Learning;
Training;
Adaptation;
Employees;
Digital Transformation
Tamayo, Jorge, Leila Doumi, Sagar Goel, Orsolya Kovács-Ondrejkovic, and Raffaella Sadun. "Reskilling in the Age of AI." Harvard Business Review 101, no. 5 (September–October 2023): 56–65.
- 2024
- Working Paper
Generative AI and Creative Problem Solving
The rapid advances in generative artificial intelligence (AI) open up attractive opportunities for creative
problem-solving through human-guided AI partnerships. To explore this potential, we initiated a
crowdsourcing challenge focused on sustainable, circular...
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Boussioux, Léonard, Jacqueline N. Lane, Miaomiao Zhang, Vladimir Jacimovic, and Karim R. Lakhani. "Generative AI and Creative Problem Solving." Harvard Business School Working Paper, No. 24-005, July 2023. (Revised March 2024.)
- August 2023
- Article
Explaining Machine Learning Models with Interactive Natural Language Conversations Using TalkToModel
By: Dylan Slack, Satyapriya Krishna, Himabindu Lakkaraju and Sameer Singh
Practitioners increasingly use machine learning (ML) models, yet models have become more complex and harder to understand. To understand complex models, researchers have proposed techniques to explain model predictions. However, practitioners struggle to use...
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Slack, Dylan, Satyapriya Krishna, Himabindu Lakkaraju, and Sameer Singh. "Explaining Machine Learning Models with Interactive Natural Language Conversations Using TalkToModel." Nature Machine Intelligence 5, no. 8 (August 2023): 873–883.
- 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.
- July–August 2023
- Article
What Smart Companies Know About Integrating AI
By: Silvio Palumbo and David Edelman
AI has the power to gather, analyze, and utilize enormous volumes of individual customer data to achieve precision and scale in personalization. The experiences of Mercury Financial, CVS Health, and Starbucks debunk the prevailing notion that extracting value from AI...
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Keywords:
AI and Machine Learning;
Customization and Personalization;
Integration;
Technology Adoption
Palumbo, Silvio, and David Edelman. "What Smart Companies Know About Integrating AI." Harvard Business Review 101, no. 4 (July–August 2023): 116–125.
- 2023
- Working Paper
Beyond the Hype: Unveiling the Marginal Benefits of 3D Virtual Tours in Real Estate
By: Mengxia Zhang and Isamar Troncoso
3D virtual tours (VTs) have become a popular digital tool in real estate platforms, enabling potential buyers to virtually walk through the houses they search for online. In this paper, we study home sellers’ adoption of VTs and the VTs’ relative benefits compared to...
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Zhang, Mengxia, and Isamar Troncoso. "Beyond the Hype: Unveiling the Marginal Benefits of 3D Virtual Tours in Real Estate." Harvard Business School Working Paper, No. 24-003, July 2023.
- July 2023
- Supplement
Honeycomb (B): Jumping on The Generative AI Bandwagon?
By: Jeffrey J. Bussgang and Kumba Sennaar
Honeycomb, an audio app enabling users to record stories and save family memories, considers pivoting to embrace generative AI. What should the co-founders business model look like if they pursued this new direction?
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- July 2023 (Revised August 2023)
- Case
Revenue Recognition at Stride Funding: Making Sense of Revenues for a Fintech Startup
By: Paul M. Healy and Jung Koo Kang
The case explores the challenges of revenue recognition and financial reporting for Stride Funding (Stride), a fintech startup that has disrupted the student loan market. Stride leveraged proprietary machine learning and financial models to underwrite alternative...
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Keywords:
Revenue Recognition;
Financial Reporting;
Entrepreneurial Finance;
Business Startups;
Growth and Development Strategy;
Governance Compliance;
Accrual Accounting;
Financial Services Industry;
United States
Healy, Paul M., and Jung Koo Kang. "Revenue Recognition at Stride Funding: Making Sense of Revenues for a Fintech Startup." Harvard Business School Case 124-015, July 2023. (Revised August 2023.)
- 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.
- July 2023 (Revised July 2023)
- Background Note
Generative AI Value Chain
By: Andy Wu and Matt Higgins
Generative AI refers to a type of artificial intelligence (AI) that can create new content (e.g., text, image, or audio) in response to a prompt from a user. ChatGPT, Bard, and Claude are examples of text generating AIs, and DALL-E, Midjourney, and Stable Diffusion are...
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Keywords:
AI;
Artificial Intelligence;
Model;
Hardware;
Data Centers;
AI and Machine Learning;
Applications and Software;
Analytics and Data Science;
Value
Wu, Andy, and Matt Higgins. "Generative AI Value Chain." Harvard Business School Background Note 724-355, July 2023. (Revised July 2023.)
- June 2023 (Revised July 2023)
- Case
Social Media Background Screening at Fama Technologies
By: Joseph Pacelli, Jillian Grennan and Alexis Lefort
Fama Technologies is an online screening company that uses AI to analyze job applicants' publicly available online content for signs of risk and culture fit. The case opens with Ben Mones, founder and CEO, looking to secure funding from venture firms. He is running...
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Keywords:
Human Resources;
Recruitment;
Retention;
Selection and Staffing;
Organizational Culture;
Talent and Talent Management;
AI and Machine Learning;
Social Media;
Venture Capital;
Entrepreneurship;
United States
Pacelli, Joseph, Jillian Grennan, and Alexis Lefort. "Social Media Background Screening at Fama Technologies." Harvard Business School Case 123-010, June 2023. (Revised July 2023.)
- June 20, 2023
- Article
Cautious Adoption of AI Can Create Positive Company Culture
By: Joseph Pacelli and Jonas Heese
Pacelli, Joseph, and Jonas Heese. "Cautious Adoption of AI Can Create Positive Company Culture." CMR Insights (June 20, 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.
- June 19, 2023
- Article
Should You Start a Generative AI Company?
Many entrepreneurs are considering starting companies that leverage the latest generative AI technology, but they must ask themselves whether they have what it takes to compete on increasingly commoditized foundational models, or whether they should instead...
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De Freitas, Julian. "Should You Start a Generative AI Company?" Harvard Business Review (website) (June 19, 2023).
- 2023
- Working Paper
Auditing Predictive Models for Intersectional Biases
By: Kate S. Boxer, Edward McFowland III and Daniel B. Neill
Predictive models that satisfy group fairness criteria in aggregate for members of a protected class, but do not guarantee subgroup fairness, could produce biased predictions for individuals at the intersection of two or more protected classes. To address this risk, we...
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Boxer, Kate S., Edward McFowland III, and Daniel B. Neill. "Auditing Predictive Models for Intersectional Biases." Working Paper, June 2023.
- 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...
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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
- Article
Provable Detection of Propagating Sampling Bias in Prediction Models
By: Pavan Ravishankar, Qingyu Mo, Edward McFowland III and Daniel B. Neill
With an increased focus on incorporating fairness in machine learning models, it becomes imperative not only to assess and mitigate bias at each stage of the machine learning pipeline but also to understand the downstream impacts of bias across stages. Here we consider...
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Ravishankar, Pavan, Qingyu Mo, Edward McFowland III, and Daniel B. Neill. "Provable Detection of Propagating Sampling Bias in Prediction Models." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (2023): 9562–9569. (Presented at the 37th AAAI Conference on Artificial Intelligence (2/7/23-2/14/23) in Washington, DC.)
- 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.