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- All HBS Web
(1,039)
- People (1)
- News (187)
- Research (673)
- Events (13)
- Multimedia (3)
- Faculty Publications (552)
Show Results For
- All HBS Web
(1,039)
- People (1)
- News (187)
- Research (673)
- Events (13)
- Multimedia (3)
- Faculty Publications (552)
- 2020
- Book
Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World
- 2020
- Working Paper
Design in the Age of Artificial Intelligence
- Web
Publications - Faculty & Research
Eliminating unintended bias in personalized policies using Bias Eliminating Adapted Trees (BEAT) - PNAS
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... View Details
Robert J. Dolan
Robert J. Dolan is the Baker Foundation Professor at Harvard Business School. He received his Ph.D. from the University of Rochester and began his academic career in 1976 as a faculty member at the Graduate School of Business of the University of Chicago. He joined... View Details
- 09 Mar 2016
- Lessons from the Classroom
In This Classroom, Beer Can Improve Your Grade
- 2023
- Chapter
Marketing Through the Machine’s Eyes: Image Analytics and Interpretability
- September 2023 (Revised April 2024)
- Case
Atomwise: Strategic Opportunities in AI for Pharma
- 2019
- Book
Fintech, Small Business & the American Dream: How Technology Is Transforming Lending and Shaping a New Era of Small Business Opportunity
- 2023
- Working Paper
Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality
- Web
HBS Working Knowledge – Harvard Business School Faculty Research
- Winter 2016
- Article
Analytics for an Online Retailer: Demand Forecasting and Price Optimization
- 08 Mar 2011
- First Look
First Look: March 8
- Article
Eliminating Unintended Bias in Personalized Policies Using Bias-Eliminating Adapted Trees (BEAT)
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
- 03 Apr 2025
- HBS Seminar
Ziad Obermeyer, UC Berkeley School of Public Health
- 2023
- Working Paper
In-Context Unlearning: Language Models as Few Shot Unlearners
- March 2021
- Case
VideaHealth: Building the AI Factory
Graphic Packaging: Project Cowboy
- 06 Jun 2017
- First Look
First Look at New Research and Ideas: June 6, 2017
- 05 Feb 2024
- Research & Ideas