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13 Aug 2024
Op-Ed

Can AI Save Physicians from Burnout?

than putting them at odds, allowing AI to be used for the greater good. Many health care organizations are starting to use AI AI, which encompasses machines View Details

Keywords: by Susanna Gallani, Lidia Moura, and Katie Sonnefeldt; Health

2025
Working Paper

Global Evidence on Gender Gaps and Generative AI

By: Nicholas G. Otis, Solène Delecourt, Katelynn Cranney and Rembrand Koning

Generative AI has the potential to transform productivity and reduce inequality, but only if adopted broadly. In this paper, we show that recently identified gender gaps in generative AI use are nearly universal. Synthesizing data from 18 studies covering more than... View Details

Keywords: AI and Machine Learning; Gender; Equality and Inequality; Technology Adoption; Behavior

February 2018 (Revised June 2021)
Case

New Constructs: Disrupting Fundamental Analysis with Robo-Analysts

By: Charles C.Y. Wang and Kyle Thomas

This case highlights the business challenges associated with a financial technology firm, New Constructs, that created a technology that can quickly parse complicated public firm financials to paint a clearer economic picture of firms, remove accounting distortions,... View Details

Keywords: Fundamental Analysis; Machine Learning; Robo-analysts; Financial Statements; Financial Reporting; Analysis; Information Technology; Accounting Industry; Financial Services Industry; Information Technology Industry; North America; Tennessee

Article

Towards Robust and Reliable Algorithmic Recourse

By: Sohini Upadhyay, Shalmali Joshi and Himabindu Lakkaraju

As predictive models are increasingly being deployed in high-stakes decision making (e.g., loan approvals), there has been growing interest in post-hoc techniques which provide recourse to affected individuals. These techniques generate recourses under the assumption... View Details

Keywords: Machine Learning Models; Algorithmic Recourse; Decision Making; Forecasting and Prediction

August 2023
Case

Beamery: Using Skills and AI to Modernize HR

By: Boris Groysberg, Alexis Lefort, Susan Pinckney and Carolina Bartunek

Unicorn human relationships startup Beamery evaluates it's growth versus depth strategy as its strategic partners and customers could become future competitors in a quickly changing AI based human resources and talent management industry View Details

January 2024
Case

The Financial Times (FT) and Generative AI

By: Andrew Rashbass, Ramon Casadesus-Masanell and Jordan Mitchell

In September 2023, John Ridding, CEO of the Financial Times, was considering the possible impact of Generative AI on the industry and his business. Having navigated successfully the seismic shift from print to digital, and reporting record results, the company... View Details

Keywords: AI and Machine Learning; Technology Adoption; Change Management; Journalism and News Industry

August 2021 (Revised November 2024)
Case

Intenseye: Powering Workplace Health and Safety with AI (A)

By: Michael W. Toffel and Youssef Abdel Aal

Intenseye was a Turkey-based technology startup that deployed machine learning algorithms to workplace camera feeds in order to identify unsafe worker actions and unsafe working conditions, in order to help improve worker safety. The case describes how Intenseye’s... View Details

Keywords: Privacy; Product Development; Operations; Technological Innovation; Value Creation; Production; Distribution; Safety; Risk and Uncertainty; Technology Industry; Manufacturing Industry; Distribution Industry; Turkey; Middle East; United States

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

2023
Book

Beyond AI: ChatGPT, Web3, and the Business Landscape of Tomorrow

By: Ken Huang, Yang Wang, Feng Zhu, Xi Chen and Chunxiao Xing

This book explores the transformative potential of ChatGPT, Web3, and their impact on productivity and various industries. It delves into Generative AI (GenAI) and its representative platform ChatGPT, their synergy with Web3, and how they can revolutionize business... View Details

Keywords: Generative Ai; AI and Machine Learning; Ethics; Technology Adoption

April 2024 (Revised December 2024)
Case

Anthropic: Building Safe AI

By: Shikhar Ghosh and Shweta Bagai

In late 2024, Anthropic, a leading AI safety and research company, achieved a significant breakthrough with computer use capabilities that allowed AI to interact with computers like humans. Co-founded by former OpenAI employees and known for its generative AI... View Details

Keywords: AI and Machine Learning; Corporate Accountability; Corporate Social Responsibility and Impact; Business Growth and Maturation; Corporate Strategy; Technology Industry; United States

March 2024 (Revised August 2024)
Case

Darktrace: Scaling Cybersecurity and AI (A)

By: Jeffrey F. Rayport and Alexis Lefort

In 2023, Darktrace CEO Poppy Gustafsson was contemplating her growth strategy at a leading U.K.-based cybersecurity venture, launched in 2013 by a group of anti-terror cyber specialists, University of Cambridge mathematicians, and artificial intelligence (AI) experts.... View Details

Keywords: Technology; Talent; Scaling; Entrepreneurship; Cybersecurity; Leadership; Business Growth and Maturation; Recruitment; Resignation and Termination; AI and Machine Learning; Growth and Development Strategy; Organizational Culture; Going Public; Technology Industry; United Kingdom; Europe; United States

March 2024
Teaching Note

CoPilot(s): Generative AI at Microsoft and GitHub

By: Frank Nagle and Maria P. Roche

This teaching note is the companion to case N9-624-010 CoPilot(s): Generative AI at Microsoft and GitHub, which takes place in late 2021. The case briefly describes the history of both GitHub and Microsoft with a particular focus on open source software (OSS)—software... View Details

Keywords: Mergers and Acquisitions; AI and Machine Learning; Applications and Software; Technological Innovation; Product Launch; Open Source Distribution; Product Development; Commercialization; Competition; Resource Allocation; Technology Industry

June 2024
Teaching Note

Beamery: Using Skills and AI to Modernize HR

By: Boris Groysberg, David Lane, Susan Pinckney and Alexis Lefort

Teaching Note for HBS Case No. 424-004. Unicorn human relationships startup Beamery evaluates it growth versus depth strategy as its strategic partners and customers could become future competitors in a quickly changing AI based human resources and talent management... View Details

2024
Working Paper

AI Companions Reduce Loneliness

By: Julian De Freitas, Ahmet K. Uğuralp, Zeliha O. Uğuralp and Stefano Puntoni

Chatbots are now able to engage in sophisticated conversations with consumers in the domain of relationships, providing a potential coping solution to widescale societal loneliness. Behavioral research provides little insight into whether these applications are... View Details

Keywords: AI and Machine Learning; Technological Innovation; Behavior; Well-being

2024
Working Paper

The Value of AI Innovations

By: Wilbur Xinyuan Chen, Terrence Tianshuo Shi and Suraj Srinivasan

We study the value of AI innovations as it diffuses across general and application sectors, using the United States Patent and Trademark Office’s (USPTO) AI patent dataset. Investors value these innovations more than others, as AI patents exhibit a 9% value premium,... View Details

Keywords: AI and Machine Learning; Valuation; Technological Innovation; Open Source Distribution; Patents; Policy; Knowledge Sharing; Technology Industry

January–February 2025
Article

Why People Resist Embracing AI

By: Julian De Freitas

The success of AI depends not only on its capabilities, which are becoming more advanced each day, but on people’s willingness to harness them. Unfortunately, many people view AI negatively, fearing it will cause job losses, increase the likelihood that their personal... View Details

Keywords: AI and Machine Learning; Technology Adoption; Perception

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January 2025
Article

Reducing Prejudice with Counter-stereotypical AI

By: Erik Hermann, Julian De Freitas and Stefano Puntoni

Based on a review of relevant literature, we propose that the proliferation of AI with human-like and social features presents an unprecedented opportunity to address the underlying cognitive and affective drivers of prejudice. An approach informed by the psychology of... View Details

Keywords: Prejudice and Bias; AI and Machine Learning; Interpersonal Communication; Social and Collaborative Networks

March 2024
Teaching Note

'Storrowed': A Generative AI Exercise

By: Mitchell Weiss

Teaching Note for HBS Exercise No. 824-188. “Storrowed” is an exercise to help participants raise their proficiency with generative AI. It begins by highlighting a problem: trucks getting wedged underneath bridges in Boston, Massachusetts on the city’s Storrow Drive.... View Details

Keywords: AI and Machine Learning; Entrepreneurship; Innovation and Invention; Government Administration; Transportation Industry; Public Administration Industry

March 2019
Teaching Note

Numenta: Inventing and (or) Commercializing AI

By: David B. Yoffie

This teaching notes accompanies the Numenta case, HBS No. 716-469. The focus is how to scale a new artificial intelligence technology, how to build a platform and overcome chicken-or-the-egg problems, and how to utilize open source software and licensing. View Details

Keywords: Artificial Intelligence; Strategy; Information Technology; Technological Innovation; Commercialization; AI and Machine Learning

Article

Faithful and Customizable Explanations of Black Box Models

By: Himabindu Lakkaraju, Ece Kamar, Rich Caruana and Jure Leskovec

As predictive models increasingly assist human experts (e.g., doctors) in day-to-day decision making, it is crucial for experts to be able to explore and understand how such models behave in different feature subspaces in order to know if and when to trust them. To... View Details

Keywords: Interpretable Machine Learning; Black Box Models; Decision Making; Framework

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