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  • June 2022
  • Article

The Use and Misuse of Patent Data: Issues for Finance and Beyond
By: Josh Lerner and Amit Seru
Patents and citations are powerful tools for understanding innovation increasingly used in financial economics (and management research more broadly). Biases may result, however, from the interactions between the truncation of patents and citations and the changing...  View Details
Keywords: Patents; Analytics and Data Science; Corporate Finance; Research
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  • May 2022
  • Case

AWS and Amazon SageMaker (A): The Commercialization of Machine Learning Services
By: Karim R. Lakhani, Shane Greenstein and Kerry Herman
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  • May 2022
  • Supplement

AWS and Amazon SageMaker (B): The Commercialization of Machine Learning Services
By: Karim R. Lakhani, Shane Greenstein and Kerry Herman
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  • May 2022
  • Supplement

AWS and Amazon SageMaker (C): The Commercialization of Machine Learning Services
By: Karim R. Lakhani, Shane Greenstein and Kerry Herman
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  • May 2022
  • Supplement

Borusan CAT: Monetizing Prediction in the Age of AI (B)
By: Navid Mojir and Gamze Yucaoglu
Borusan Cat is an international distributor of Caterpillar heavy machines. In 2021, it had been three years since Ozgur Gunaydin (CEO) and Esra Durgun (Director of Strategy, Digitization, and Innovation) started working on Muneccim, the company’s predictive AI tool....  View Details
Keywords: AI and Machine Learning; Commercialization; Technology Adoption; Industrial Products Industry; Turkey; Middle East
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  • May 2022
  • Case

LOOP: Driving Change in Auto Insurance Pricing
By: Elie Ofek and Alicia Dadlani
John Henry and Carey Anne Nadeau, co-founders and co-CEOs of LOOP, an insurtech startup based in Austin, Texas, were on a mission to modernize the archaic $250 billion automobile insurance market. They sought to create equitably priced insurance by eliminating pricing...  View Details
Keywords: AI and Machine Learning; Technological Innovation; Equality and Inequality; Prejudice and Bias; Insurance Industry; Financial Services Industry
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  • Article

Developing a Digital Mindset: How to Lead Your Organization into the Age of Data, Algorithms, and AI
By: Tsedal Neeley and Paul Leonardi
Learning new technological skills is essential for digital transformation. But it is not enough. Employees must be motivated to use their skills to create new opportunities. They need a digital mindset: a set of attitudes and behaviors that enable people and...  View Details
Keywords: Machine Learning; AI; Information Technology; Transformation; Competency and Skills; Employees; Technology Adoption; Leading Change; Digital Transformation
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  • 2022
  • Article

Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis.
By: Martin Pawelczyk, Chirag Agarwal, Shalmali Joshi, Sohini Upadhyay and Himabindu Lakkaraju
As machine learning (ML) models become more widely deployed in high-stakes applications, counterfactual explanations have emerged as key tools for providing actionable model explanations in practice. Despite the growing popularity of counterfactual explanations, a...  View Details
Keywords: Machine Learning Models; Counterfactual Explanations; Adversarial Examples; Mathematical Methods
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  • 2022
  • Book

The Digital Mindset: What It Really Takes to Thrive in the Age of Data, Algorithms, and AI
By: Paul Leonardi and Tsedal Neeley
The pressure to "be digital" has never been greater, but you can meet the challenge. The digital revolution is here, changing how work gets done, how industries are structured, and how people from all walks of life work, behave, and relate to each other. To thrive...  View Details
Keywords: Digital; Artificial Intelligence; Big Data; Digital Transformation; Technological Innovation; Transformation; Learning; Competency and Skills
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  • April 2022
  • Article

AI Insurance: How Liability Insurance Can Drive the Responsible Adoption of Artificial Intelligence in Health Care
By: Ariel Dora Stern, Avi Goldfarb and Timo Minssen
Despite enthusiasm about the potential to apply artificial intelligence (AI) to medicine and health care delivery, adoption remains tepid, even for the most compelling technologies. In this article, the authors focus on one set of challenges to AI adoption: those...  View Details
Keywords: Artificial Intelligence; Medicine; Health Care and Treatment; Legal Liability; Insurance; Technology Adoption; AI and Machine Learning
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  • March 2022 (Revised March 2022)
  • Module Note

Prediction & Machine Learning
By: Iavor I. Bojinov, Michael Parzen and Paul J. Hamilton
This note provides an introduction to machine learning for an introductory data science course. The note begins with a description of supervised, unsupervised, and reinforcement learning. Then, the note provides a brief explanation of the difference between traditional...  View Details
Keywords: Machine Learning; Data Science; Learning; Analytics and Data Science; Performance Evaluation
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  • March 2022
  • Case

Unilever: Remote Work in Manufacturing
By: Prithwiraj Choudhury and Susie L. Ma
In December 2021, Unilever—one of the world’s largest producers of consumer goods—was in the midst of a pilot project to digitize its manufacturing facilities and enable remote work for factory employees. This was possible because of an earlier project to retrofit a...  View Details
Keywords: Change; Globalization; Information Technology; Technology Adoption; Human Resources; Jobs and Positions; Operations; Education; Training; Manufacturing Industry
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  • Article

Eliminating Unintended Bias in Personalized Policies Using Bias-Eliminating Adapted Trees (BEAT)
By: Eva Ascarza and Ayelet Israeli

An inherent risk of algorithmic personalization is disproportionate targeting of individuals from certain groups (or demographic characteristics such as gender or race), even when the decision maker does not intend to discriminate based on those “protected”...  View Details

Keywords: Algorithm Bias; Personalization; Targeting; Generalized Random Forests (GRF); Discrimination; Customization and Personalization; Decision Making; Fairness; Mathematical Methods
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  • March 2022
  • Article

Winner Takes All? Tech Clusters, Population Centers, and the Spatial Transformation of U.S. Invention
By: Brad Chattergoon and William R. Kerr
U.S. invention has become increasingly concentrated around major tech centers since the 1970s, with implications for how much cities across the country share in concomitant local benefits. Is invention becoming a winner-takes-all race? We explore the rising spatial...  View Details
Keywords: Clusters; Invention; Agglomeration; Artificial Intelligence; Innovation and Invention; Patents; Applications and Software; Industry Clusters; AI and Machine Learning
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  • January–February 2022
  • Article

Algorithm-Augmented Work and Domain Experience: The Countervailing Forces of Ability and Aversion
By: Ryan Allen and Prithwiraj Choudhury
How does a knowledge worker’s level of domain experience affect their algorithm-augmented work performance? We propose and test theoretical predictions that domain experience has countervailing effects on algorithm-augmented performance: on one hand, domain experience...  View Details
Keywords: Automation; Domain Experience; Algorithmic Aversion; Experts; Algorithms; Machine Learning; Future Of Work; Employees; Experience and Expertise; Decision Making; Performance
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  • February 2022
  • Teaching Note

Borusan CAT: Monetizing Prediction in the Age of AI
By: Navid Mojir
Teaching Note for HBS Case No. 521-053.  View Details
Keywords: Monetization Strategy; Artificial Intelligence; Forecasting and Prediction; Applications and Software; Technological Innovation; Marketing; Segmentation; AI and Machine Learning; Construction Industry; Turkey
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  • February 2022
  • Case

Nuritas
By: Mitchell Weiss, Satish Tadikonda, Vincent Marie Dessain and Emer Moloney
Nora Khaldi had built a technology “to unlock the power of nature” in the service of extending human lifespan and improving health, and now in April 2020 was debating telling her Board of Directors she wanted to put on ice some of her discoveries. Nuritas, the company...  View Details
Keywords: Cash Burn; Cash Flow Analysis; Pharmaceutical Companies; Founder; Artificial Intelligence; AI; Entrepreneurship; Health Testing and Trials; Health Care and Treatment; Decision Making; Market Entry and Exit; AI and Machine Learning; Pharmaceutical Industry
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  • January 2022
  • Article

Artificial Intelligence, Data-Driven Learning, and the Decentralized Structure of Platform Ecosystems
By: David R. Clough and Andy Wu
Gregory, Henfridsson, Kaganer, and Kyriakou (2020) highlight the important role of data and AI as strategic resources that platforms may use to enhance user value. However, their article overlooks a significant conceptual distinction: the installed base of...  View Details
Keywords: Artificial Intelligence; Data Strategy; Ecosystem; Value Capture; Digital Platforms; Analytics and Data Science; Strategy; Learning; Value Creation; AI and Machine Learning; Technology Industry; Information Technology Industry; Video Game Industry; Advertising Industry
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  • 2021
  • Working Paper

CRM and AI in Time of Crisis
By: Michelle Y. Lu and Navid Mojir
A crisis can affect the incentives of various players within a firm’s multi-layered sales and marketing organization (e.g., headquarters and branches of a bank). Such shifts can result in sales decisions against the firm’s best interests. Motivated by the backlash to...  View Details
Keywords: CRM; Artificial Intelligence; AI; B2B Marketing; Decision Authority; Crisis Marketing; Intra-organizational Conflict; COVID-19 Pandemic; Customer Relationship Management; Technological Innovation; Decision Making; Strategy; Health Pandemics; Crisis Management; AI and Machine Learning
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  • Article

A Prescriptive Analytics Framework for Optimal Policy Deployment Using Heterogeneous Treatment Effects
By: Edward McFowland III, Sandeep Gangarapu, Ravi Bapna and Tianshu Sun
We define a prescriptive analytics framework that addresses the needs of a constrained decision-maker facing, ex ante, unknown costs and benefits of multiple policy levers. The framework is general in nature and can be deployed in any utility maximizing context, public...  View Details
Keywords: Prescriptive Analytics; Heterogeneous Treatment Effects; Optimization; Observed Rank Utility Condition (OUR); Between-treatment Heterogeneity; Machine Learning; Decision Making; Analysis; Mathematical Methods
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