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- All HBS Web
(954)
- People (1)
- News (155)
- Research (635)
- Events (13)
- Multimedia (3)
- Faculty Publications (542)
- Web
Frequently Asked Questions | HBS Online
hypotheses and insights from visualization; identify data mistakes or missing components; and, speak the language of data science across themes such as forecasting, linear regressions, and machine learning... View Details
- 26 Mar 2018
- Research & Ideas
To Motivate Employees, Give an Unexpected Bonus (or Penalty)
employees make or how many units they produce. “The objective performance measures don’t take into consideration whether the machine broke down or whether someone is still learning the job,” Gallani... View Details
- October 2019
- Case
Leading Bank Leumi into the Future
By: Joshua D. Margolis, Allison M. Ciechanover, Nicole Keller and Danielle Golan
An unlikely but highly effective leader of a traditional bank, Rakefet Russak-Aminoach, simultaneously leads a classic change effort and an unconventional effort to innovate. She focuses her initial energy on making the bank more efficient in the face of industry... View Details
Keywords: Mobile Banking; Digital Banking; Fintech; Startup; Financial Services; Artificial Intelligence; Innovation; Efficiency; Organizational Change; Personal Development; Female Ceo; Banks and Banking; Mobile and Wireless Technology; Leadership; Organizational Change and Adaptation; Innovation and Invention; Disruption; Information Technology; Opportunities; Performance Effectiveness; Personal Development and Career; AI and Machine Learning; Financial Services Industry; Banking Industry; Israel
Margolis, Joshua D., Allison M. Ciechanover, Nicole Keller, and Danielle Golan. "Leading Bank Leumi into the Future." Harvard Business School Case 420-063, October 2019.
- Web
Scaling Work - Research Computing Services
can be run in parallel ....and are often used for Machine Learning types of'embarrassingly parallel' tasks (== a huge task can be broken down into many smaller ones that are completely independent of one... View Details
- Web
National Markets - The Art of American Advertising
Clubs Faculty & Research Business & Environment Business History Christensen Center for Teaching & Learning Entrepreneurship Faculty & Research Global Healthcare HBS Working Knowledge Institute for Strategy & Competitiveness Leadership... View Details
- 27 Sep 2021
- Blog Post
Working to Change the Food System
living in San Francisco working at a data science startup as a product manager. I have always gravitated toward technology and science throughout my life, and machine learning, artificial intelligence, and data science were fascinating as... View Details
- 28 Apr 2022
- Research & Ideas
Can You Buy Creativity in the Gig Economy?
gauged by monitoring nearly 1 million reader reviews using, in part, supervised machine learning in addition to searching for keywords such as “original,” “creative,” “surprisingly clever,” “innovative,” and... View Details
Keywords: by Pamela Reynolds
- February 2024 (Revised January 2025)
- 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... View Details
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. (Revised January 2025.)
- 12 Nov 2021
- News
Alumni Business Leaders on Confronting the Climate Change Challenge
global and a series of regional B Climate Collectives designed to support businesses to share what they are learning on topics such as how to move to a regenerative business model. How might you explore ways to reimagine your business... View Details
- 01 Jun 2008
- News
You Only Thought You Were Republican
Gilbert (estimated net worth even higher, in undisclosed currencies) — which are not really their net worths, but that’s how I feel sitting there amongst them, technically “the moderator,” but truly “the rounding error.” I am doing this partly to View Details
- Web
Entrepreneurial Management Awards & Honors - Faculty & Research
Mifflin Harcourt, 2017). Scott Duke Kominers : Winner of a 2018 Webby Award for Best Use of Machine Learning from the International Academy of Digital Arts and Sciences for Streetchange with Nikhil Naik,... View Details
- 2024
- Conference Paper
Quantifying Uncertainty in Natural Language Explanations of Large Language Models
By: Himabindu Lakkaraju, Sree Harsha Tanneru and Chirag Agarwal
Large Language Models (LLMs) are increasingly used as powerful tools for several
high-stakes natural language processing (NLP) applications. Recent prompting
works claim to elicit intermediate reasoning steps and key tokens that serve as
proxy explanations for LLM... View Details
Lakkaraju, Himabindu, Sree Harsha Tanneru, and Chirag Agarwal. "Quantifying Uncertainty in Natural Language Explanations of Large Language Models." Paper presented at the Society for Artificial Intelligence and Statistics, 2024.
- 2022
- Article
Efficiently Training Low-Curvature Neural Networks
By: Suraj Srinivas, Kyle Matoba, Himabindu Lakkaraju and Francois Fleuret
Standard deep neural networks often have excess non-linearity, making them susceptible to issues such as low adversarial robustness and gradient instability. Common methods to address these downstream issues, such as adversarial training, are expensive and often... View Details
Keywords: AI and Machine Learning
Srinivas, Suraj, Kyle Matoba, Himabindu Lakkaraju, and Francois Fleuret. "Efficiently Training Low-Curvature Neural Networks." Advances in Neural Information Processing Systems (NeurIPS) (2022).
- 2023
- Article
M4: A Unified XAI Benchmark for Faithfulness Evaluation of Feature Attribution Methods across Metrics, Modalities, and Models
By: Himabindu Lakkaraju, Xuhong Li, Mengnan Du, Jiamin Chen, Yekun Chai and Haoyi Xiong
While Explainable Artificial Intelligence (XAI) techniques have been widely studied to explain predictions made by deep neural networks, the way to evaluate the faithfulness of explanation results remains challenging, due to the heterogeneity of explanations for... View Details
Keywords: AI and Machine Learning
Lakkaraju, Himabindu, Xuhong Li, Mengnan Du, Jiamin Chen, Yekun Chai, and Haoyi Xiong. "M4: A Unified XAI Benchmark for Faithfulness Evaluation of Feature Attribution Methods across Metrics, Modalities, and Models." Advances in Neural Information Processing Systems (NeurIPS) (2023).
- 2023
- Working Paper
Auditing Predictive Models for Intersectional Biases
By: Kate S. Boxer, Edward McFowland III and Daniel B. Neill
Predictive models that satisfy group fairness criteria in aggregate for members of a protected class, but do not guarantee subgroup fairness, could produce biased predictions for individuals at the intersection of two or more protected classes. To address this risk, we... View Details
Boxer, Kate S., Edward McFowland III, and Daniel B. Neill. "Auditing Predictive Models for Intersectional Biases." Working Paper, June 2023.
- March 2017
- Supplement
Donna Dubinsky, Numenta and Artificial Intelligence
By: David B. Yoffie
Donna Dubinsky, CEO of Numenta, discusses her views of the future of artificial intelligence and the strategic challenges of building a new platform. View Details
Keywords: Artificial Intelligence; Strategy; Technological Change; AI and Machine Learning; Technology Industry
Yoffie, David B. "Donna Dubinsky, Numenta and Artificial Intelligence." Harvard Business School Multimedia/Video Supplement 717-807, March 2017.
- October 2024
- Technical Note
Prompt Engineering
By: Michael Parzen and Jo Ellery
This note covers the basics of prompt engineering, a key tool for making use of modern generative AI. We discuss the principles of prompt engineering and illustrate these principles with techniques for asking questions. We further list the types of prompts that can be... View Details
Parzen, Michael, and Jo Ellery. "Prompt Engineering." Harvard Business School Technical Note 625-056, October 2024.
- April 2023
- Article
On the Privacy Risks of Algorithmic Recourse
By: Martin Pawelczyk, Himabindu Lakkaraju and Seth Neel
As predictive models are increasingly being employed to make consequential decisions, there is a growing emphasis on developing techniques that can provide algorithmic recourse to affected individuals. While such recourses can be immensely beneficial to affected... View Details
Pawelczyk, Martin, Himabindu Lakkaraju, and Seth Neel. "On the Privacy Risks of Algorithmic Recourse." Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS) 206 (April 2023).
- December 18, 2024
- Article
Is AI the Right Tool to Solve That Problem?
By: Paolo Cervini, Chiara Farronato, Pushmeet Kohli and Marshall W Van Alstyne
While AI has the potential to solve major problems, organizations embarking on such journeys of often encounter obstacles. They include a dearth of high-quality data; too many possible solutions; the lack of a clear, measurable objective; and difficulty in identifying... View Details
Cervini, Paolo, Chiara Farronato, Pushmeet Kohli, and Marshall W Van Alstyne. "Is AI the Right Tool to Solve That Problem?" Harvard Business Review (website) (December 18, 2024).