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Show Results For
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All HBS Web
(1,035)
- People (1)
- News (209)
- Research (557)
- Events (8)
- Multimedia (7)
- Faculty Publications (446)
- 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.
- 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.)
- 14 Aug 2017
- Conference Presentation
A Convex Framework for Fair Regression
By: Richard Berk, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel and Aaron Roth
We introduce a flexible family of fairness regularizers for (linear and logistic) regression problems. These regularizers all enjoy convexity, permitting fast optimization, and they span the range from notions of group fairness to strong individual fairness. By varying...
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Berk, Richard, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, and Aaron Roth. "A Convex Framework for Fair Regression." Paper presented at the 4th Workshop on Fairness, Accountability, and Transparency in Machine Learning, Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD), August 14, 2017.
- March 2023 (Revised March 2024)
- Case
Accelerating AI Adoption in the U.S. Air Force
By: Maria P. Roche and Alexander Farrow
In August 2022, the Pentagon tasked U.S. Air Force Captain Victor Lopez to launch a new office for AFWERX, an Air Force innovation unit that leveraged commercial developers and military talent to acquire advanced technologies. This task was particularly arduous because...
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Keywords:
Technological Innovation;
Organizational Design;
AI and Machine Learning;
Adoption;
Technology Adoption;
United States
Roche, Maria P., and Alexander Farrow. "Accelerating AI Adoption in the U.S. Air Force." Harvard Business School Case 723-429, March 2023. (Revised March 2024.)
Competing in the Age of AI
Marco Iansiti and Karim R. Lakhani show how reinventing the firm around data, analytics, and AI removes traditional constraints on scale, scope, and learning that have restricted business growth for hundreds of years. From... View Details
- October 2023
- Article
Improving Regulatory Effectiveness Through Better Targeting: Evidence from OSHA
By: Matthew S. Johnson, David I. Levine and Michael W. Toffel
We study how a regulator can best target inspections. Our case study is a U.S. Occupational Safety and Health Administration (OSHA) program that randomly allocated some inspections. On average, each inspection averted 2.4 serious injuries (9%) over the next five years....
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Keywords:
Safety Regulations;
Regulations;
Regulatory Enforcement;
Machine Learning Models;
Safety;
Operations;
Service Operations;
Production;
Forecasting and Prediction;
Decisions;
United States
Johnson, Matthew S., David I. Levine, and Michael W. Toffel. "Improving Regulatory Effectiveness Through Better Targeting: Evidence from OSHA." American Economic Journal: Applied Economics 15, no. 4 (October 2023): 30–67. (Profiled in the Regulatory Review.)
- 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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- 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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- September 2020
- Article
Creativity, Artificial Intelligence, and a World of Surprises
In recent years, progress has been made toward AI Creativity, which I define as the production of highly novel, yet appropriate, ideas, problem solutions, or other outputs by autonomous machines. I argue that organizational researchers of creativity and innovation...
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Keywords:
Artificial Intelligence;
AI Creativity;
Computer Science;
Organizational Behavior;
Psychology;
Creativity;
Technological Innovation;
AI and Machine Learning
Amabile, Teresa M. "Creativity, Artificial Intelligence, and a World of Surprises." Academy of Management Discoveries 6, no. 3 (September 2020): 351–354.
- 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.
- Article
Counterfactual Explanations Can Be Manipulated
By: Dylan Slack, Sophie Hilgard, Himabindu Lakkaraju and Sameer Singh
Counterfactual explanations are useful for both generating recourse and auditing fairness between groups. We seek to understand whether adversaries can manipulate counterfactual explanations in an algorithmic recourse setting: if counterfactual explanations indicate...
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Slack, Dylan, Sophie Hilgard, Himabindu Lakkaraju, and Sameer Singh. "Counterfactual Explanations Can Be Manipulated." Advances in Neural Information Processing Systems (NeurIPS) 34 (2021).
- 01 Dec 2020
- News
AI Enhances Diagnostic Care
changing the field of medicine. In the past several decades, the growing use of artificial intelligence in the health care sector has made it possible for computer systems and diagnostic machines to View Details
Keywords:
Jennifer Gillespie
- 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.
- 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.
- 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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- November 2022
- Article
A Language-Based Method for Assessing Symbolic Boundary Maintenance between Social Groups
By: Anjali M. Bhatt, Amir Goldberg and Sameer B. Srivastava
When the social boundaries between groups are breached, the tendency for people to erect and maintain symbolic boundaries intensifies. Drawing on extant perspectives on boundary maintenance, we distinguish between two strategies that people pursue in maintaining...
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Keywords:
Culture;
Machine Learning;
Natural Language Processing;
Symbolic Boundaries;
Organizations;
Boundaries;
Social Psychology;
Interpersonal Communication;
Organizational Culture
Bhatt, Anjali M., Amir Goldberg, and Sameer B. Srivastava. "A Language-Based Method for Assessing Symbolic Boundary Maintenance between Social Groups." Sociological Methods & Research 51, no. 4 (November 2022): 1681–1720.
- 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–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.
- 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.)