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Show Results For
-
All HBS Web
(981)
- People (1)
- News (194)
- Research (535)
- Events (8)
- Multimedia (6)
- Faculty Publications (424)
- December 2020
- Supplement
VIA Science (B)
By: Juan Alcácer, Rembrand Koning, Annelena Lobb and Kerry Herman
Via (a) captures the early days of the data analytics startup as founders Gounden and Ravanis considered which markets offer the right opportunities for their firm and what kinds of experiments will help them narrow their choice. Supplement Via (b) reveals the...
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Keywords:
Data Analytics;
Machine Learning;
Artificial Intelligence;
Strategy;
Business Startups;
AI and Machine Learning;
Telecommunications Industry;
Utilities Industry;
United States;
Japan
Alcácer, Juan, Rembrand Koning, Annelena Lobb, and Kerry Herman. "VIA Science (B)." Harvard Business School Supplement 721-368, December 2020.
- 26 Apr 2023
- In Practice
Is AI Coming for Your Job?
will be displaced in large numbers. Those job losses will be partially offset by job gains for machine learning specialists and emerging jobs like prompt engineers. But, once...
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- October–December 2022
- Article
Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem
By: Mochen Yang, Edward McFowland III, Gordon Burtch and Gediminas Adomavicius
Combining machine learning with econometric analysis is becoming increasingly prevalent in both research and practice. A common empirical strategy involves the application of predictive modeling techniques to "mine" variables of interest from available data, followed...
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Keywords:
Machine Learning;
Econometric Analysis;
Instrumental Variable;
Random Forest;
Causal Inference;
AI and Machine Learning;
Forecasting and Prediction
Yang, Mochen, Edward McFowland III, Gordon Burtch, and Gediminas Adomavicius. "Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem." INFORMS Journal on Data Science 1, no. 2 (October–December 2022): 138–155.
- 2020
- Working Paper
Algorithm-Augmented Work and Domain Experience: The Countervailing Forces of Ability and Aversion
By: Ryan Allen and Prithwiraj Choudhury
Past research offers mixed perspectives on whether domain experience helps or hurts algorithm-augmented work performance. To reconcile these perspectives, we theorize that domain experience affects algorithm-augmented performance via two distinct countervailing...
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Keywords:
Automation;
Domain Experience;
Algorithmic Aversion;
Experts;
Algorithms;
Machine Learning;
Decision-making;
Future Of Work;
Employees;
Experience and Expertise;
Decision Making;
Performance
Allen, Ryan, and Prithwiraj Choudhury. "Algorithm-Augmented Work and Domain Experience: The Countervailing Forces of Ability and Aversion." Harvard Business School Working Paper, No. 21-073, October 2020. (Revised September 2021.)
- 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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- January 2024 (Revised February 2024)
- Case
OpenAI: Idealism Meets Capitalism
By: Shikhar Ghosh and Shweta Bagai
In November 2023, the board of OpenAI, one of the most successful companies in the history of technology, decided to fire Sam Altman, its charismatic and influential CEO. Their decision shocked the corporate world and had people wondering why OpenAI had designed a...
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Keywords:
AI;
AI and Machine Learning;
Governing and Advisory Boards;
Ethics;
Strategy;
Technological Innovation;
Leadership
Ghosh, Shikhar, and Shweta Bagai. "OpenAI: Idealism Meets Capitalism." Harvard Business School Case 824-134, January 2024. (Revised February 2024.)
- Web
Competing in the Age of AI
Learn about the AI tools at your disposal, and how to deploy them in your organization. Machine learning isn't the...
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- February 2024
- Case
AGENTS.inc: Pathways to Growth at an AI Startup
By: Frank Nagle, Manuel Hoffmann, Karoline Ströhlein and Susan Pinckney
The case describes the history of AGENTS.inc. Despite being a small startup, with only four employees, that had never had a funding round, the company boasted an impressive client portfolio including multiple Fortune 500 companies. While AGENTS.inc had been an early...
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Keywords:
Business Growth and Maturation;
Business Model;
Business Startups;
Small Business;
Transformation;
Customer Focus and Relationships;
Decisions;
Entrepreneurship;
Venture Capital;
Financial Strategy;
AI and Machine Learning;
Digital Platforms;
Technological Innovation;
Copyright;
Management;
Growth and Development;
Market Timing;
Ownership;
Risk and Uncertainty;
Competition;
Open Source Distribution;
Entrepreneurial Finance;
Computer Industry;
Europe;
Germany
Nagle, Frank, Manuel Hoffmann, Karoline Ströhlein, and Susan Pinckney. "AGENTS.inc: Pathways to Growth at an AI Startup." Harvard Business School Case 724-444, February 2024.
- 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...
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Keywords:
Automation;
Domain Experience;
Algorithmic Aversion;
Experts;
Algorithms;
Machine Learning;
Future Of Work;
Employees;
Experience and Expertise;
Decision Making;
Performance
Allen, Ryan, and Prithwiraj Choudhury. "Algorithm-Augmented Work and Domain Experience: The Countervailing Forces of Ability and Aversion." Organization Science 33, no. 1 (January–February 2022): 149–169. ("Best PhD Student Paper" at SMS conference 2020.)
- September–October 2021
- Article
Frontiers: Can an AI Algorithm Mitigate Racial Economic Inequality? An Analysis in the Context of Airbnb
By: Shunyuan Zhang, Nitin Mehta, Param Singh and Kannan Srinivasan
We study the effect of Airbnb’s smart-pricing algorithm on the racial disparity in the daily revenue earned by Airbnb hosts. Our empirical strategy exploits Airbnb’s introduction of the algorithm and its voluntary adoption by hosts as a quasi-natural experiment. Among...
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Keywords:
Smart Pricing;
Pricing Algorithm;
Machine Bias;
Discrimination;
Racial Disparity;
Social Inequality;
Airbnb Revenue;
Revenue;
Race;
Equality and Inequality;
Prejudice and Bias;
Price;
Mathematical Methods;
Accommodations Industry
Zhang, Shunyuan, Nitin Mehta, Param Singh, and Kannan Srinivasan. "Frontiers: Can an AI Algorithm Mitigate Racial Economic Inequality? An Analysis in the Context of Airbnb." Marketing Science 40, no. 5 (September–October 2021): 813–820.
- December 2020
- Case
VIA Science (A)
By: Juan Alcácer, Rembrand Koning, Annelena Lobb and Kerry Herman
Via (a) captures the early days of the data analytics startup as founders Gounden and Ravanis considered which markets offer the right opportunities for their firm and what kinds of experiments will help them narrow their choice. Supplement Via (b) reveals the...
View Details
Keywords:
Data Analytics;
Machine Learning;
Artificial Intelligence;
Strategy;
Business Startups;
Markets;
AI and Machine Learning;
Telecommunications Industry;
Utilities Industry;
United States;
Japan
Alcácer, Juan, Rembrand Koning, Annelena Lobb, and Kerry Herman. "VIA Science (A)." Harvard Business School Case 721-367, December 2020.
- October 2018
- Case
American Family Insurance and the Artificial Intelligence Opportunity
By: Rajiv Lal and Scott Johnson
Keywords:
Artificial Intelligence;
Machine Learning;
Automation;
Analytics;
American Family;
American Family Insurance;
Insurance;
Business Organization;
Transformation;
Talent and Talent Management;
Employee Relationship Management;
Innovation Strategy;
Job Cuts and Outsourcing;
Risk and Uncertainty;
Mobile and Wireless Technology;
Technology Adoption;
Internet and the Web;
Applications and Software;
Corporate Strategy;
AI and Machine Learning;
Digital Transformation;
Insurance Industry;
Technology Industry;
Wisconsin
- 20 Oct 2022 - 22 Oct 2022
- Talk
Stigma Against AI Companion Applications
By: Julian De Freitas, A. Ragnhildstveit and A.K. Uğuralp
- September 29, 2023
- Article
Eliminating Algorithmic Bias Is Just the Beginning of Equitable AI
By: Simon Friis and James Riley
When it comes to artificial intelligence and inequality, algorithmic bias rightly receives a lot of attention. But it’s just one way that AI can lead to inequitable outcomes. To truly create equitable AI, we need to consider three forces through which it might make...
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Friis, Simon, and James Riley. "Eliminating Algorithmic Bias Is Just the Beginning of Equitable AI." Harvard Business Review (website) (September 29, 2023).
- January 2018 (Revised February 2023)
- Teaching Note
The Future of Patent Examination at the USPTO
This teaching note pairs with the case entitled: “The Future of Patent Examination at the USPTO” (case no. 617-027).
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- October 2015 (Revised October 2016)
- Case
Building Watson: Not So Elementary, My Dear! (Abridged)
By: Willy C. Shih
This case is set inside IBM Research's efforts to build a computer that can successfully take on human challengers playing the game show Jeopardy! It opens with the machine named Watson offering the incorrect answer "Toronto" to a seemingly simple question during the...
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Keywords:
Analytics;
Big Data;
Business Analytics;
Product Development Strategy;
Machine Learning;
Machine Intelligence;
Artificial Intelligence;
Product Development;
AI and Machine Learning;
Information Technology;
Analytics and Data Science;
Information Technology Industry;
United States
Shih, Willy C. "Building Watson: Not So Elementary, My Dear! (Abridged)." Harvard Business School Case 616-025, October 2015. (Revised October 2016.)
- 2017
- Working Paper
The Need for Speed: Effects of Uncertainty Reduction in Patenting
By: Mike Horia Teodorescu
Patents are essential in commerce to establish property rights for ideas and to give equal protection to firms that develop new technologies. Young firms especially depend on the protection of intellectual property to bring a product from concept to market. However,...
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- Blog
Is AI Coming for Your Job?
be displaced in large numbers. Those job losses will be partially offset by job gains for machine learning specialists and emerging jobs like prompt engineers. But, once...
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- 15 Nov 2018
- News
Don’t Be Afraid of AI
Dubinsky: And we get example after example, I just used that one 'cause it's so visual, of ways in which there are things we cannot do today, or it's very dangerous for humans today, that if we had a machine...
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