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- May 2022 (Revised July 2023)
- Case
Altibbi: Revolutionizing Telehealth Using AI
- May 2022 (Revised June 2024)
- 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
- May 2022
- Case
Timnit Gebru: 'SILENCED No More' on AI Bias and The Harms of Large Language Models
By: Tsedal Neeley and Stefani Ruper
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 she accepted your resignation.” Heart... View Details
- May 2022 (Revised July 2022)
- Case
The Voice War Continues: Hey Google vs. Alexa vs. Siri in 2022
By: David B. Yoffie and Daniel Fisher
In 2022, after five years of pursuing a new "AI-first" strategy, Google had captured a sizeable share of the American and global markets for voice assistants. Google Assistant was used by hundreds of millions of users around the world, but Amazon retained the largest... View Details
- May 2022
- Article
Coins for Bombs: The Predictive Ability of On-Chain Transfers for Terrorist Attacks
By: Dan Amiram, Evgeny Lyandres and Daniel Rabetti
This study examines whether we can learn from the behavior of blockchain-based transfers to predict the financing of terrorist attacks. We exploit blockchain transaction transparency to map millions of transfers for hundreds of large on-chain service providers. The... View Details
- 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
- 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
- March 2022 (Revised January 2025)
- Technical Note
Prediction & Machine Learning
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
- 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
- February 2022 (Revised February 2023)
- Case
TikTok in 2020: Super App or Supernova? (Abridged)
By: Jeffrey F. Rayport, Dan Maher and Dan O'Brien
TikTok’s parent company, ByteDance, was launched in 2012 around a simple idea—helping users entertain themselves on their smartphones while on the Beijing Subway. In less than a decade, it had become one of the world’s most valuable private companies, with investors... View Details
- 2022
- Working Paper
The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective
By: Satyapriya Krishna, Tessa Han, Alex Gu, Javin Pombra, Shahin Jabbari, Steven Wu and Himabindu Lakkaraju
As various post hoc explanation methods are increasingly being leveraged to explain complex models in high-stakes settings, it becomes critical to develop a deeper understanding of if and when the explanations output by these methods disagree with each other, and how... View Details
- February 2022 (Revised September 2022)
- Case
InstaDeep: AI Innovation Born in Africa (A)
By: Shikhar Ghosh and Esel Çekin
Karim Beguir and Zohra Slim were the co-founders of InstaDeep, a deep tech startup focusing on artificial intelligence (AI) solutions. Instadeep was one of the few companies globally that were partnering with DeepMind, an AI subsidiary of Google [Alphabet Inc.].... View Details
- February 2022 (Revised July 2022)
- Supplement
InstaDeep: AI Innovation Born in Africa (B)
By: Shikhar Ghosh and Esel Çekin
Karim Beguir and Zohra Slim were the co-founders of InstaDeep, a deep tech startup focusing on artificial intelligence (AI) solutions. Instadeep was one of the few companies globally that were partnering with DeepMind, an AI subsidiary of Google [Alphabet Inc.].... View Details
- February 2022 (Revised November 2022)
- Case
Nuritas
By: Mitchell Weiss, Satish Tadikonda, Vincent 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
- 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
- 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... View Details
- 2022
- Working Paper
TalkToModel: Explaining Machine Learning Models with Interactive Natural Language Conversations
By: Dylan Slack, Satyapriya Krishna, Himabindu Lakkaraju and Sameer Singh
Practitioners increasingly use machine learning (ML) models, yet they have become more complex and harder to understand. To address this issue, researchers have proposed techniques to explain model predictions. However, practitioners struggle to use explainability... View Details
- December 2021
- Teaching Note
Intenseye: Powering Workplace Health and Safety with AI
By: Michael W. Toffel and Youssef Abdel Aal
Teaching Note for HBS Case No. 622-037. View Details
- December 2021 (Revised February 2025)
- Supplement
Intenseye: Powering Workplace Health and Safety with AI – PowerPoint Supplement
By: Michael W. Toffel, Youssef Abdel Aal and Sadika El Hariri
- December 2021
- Case
Slice Labs: Creating a Fraud-free Online Insurance Platform
By: Amit Goldenberg, Max Bazerman and Ruth Page
"Slice Labs: Creating a Fraud-Free Online Insurance Platform" engages students with the challenge of how to influence other parties to not engage in fraud in the context of digital insurance. The case is centered around Slice, a digital insurance company that was... View Details
Keywords: Technology; Insurance; Digitization; Honesty; Negotiation; Fraud; Ethics; Negotiation Process; Negotiation Tactics; Negotiation Types; Social Psychology; Conflict and Resolution; Trust; Fairness; Moral Sensibility; Values and Beliefs; Crime and Corruption; Insurance Industry; Technology Industry; United States; Canada