Filter Results
:
(864)
Show Results For
-
All HBS Web
(864)
- People (1)
- News (152)
- Research (549)
- Events (10)
- Multimedia (3)
- Faculty Publications (453)
Show Results For
-
All HBS Web
(864)
- People (1)
- News (152)
- Research (549)
- Events (10)
- Multimedia (3)
- Faculty Publications (453)
- 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...
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.
- 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
- 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
- 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
- Web
Entrepreneurial Management Awards & Honors - Faculty & Research
Award Bronze Medal in the Economics category for The Wisdom of Finance: Discovering Humanity in the World of Risk and Return (Houghton Mifflin Harcourt, 2017). Scott Duke Kominers : Winner of a 2018 Webby Award for Best Use of Machine...
View Details
- 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.
- 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).
- 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).
- November 2, 2021
- Article
The Cultural Benefits of Artificial Intelligence in the Enterprise
By: Sam Ransbotham, François Candelon, David Kiron, Burt LaFountain and Shervin Khodabandeh
The 2021 MIT SMR-BCG report identifies a wide range of AI-related cultural benefits at both the team and organizational levels. Whether it’s reconsidering business assumptions or empowering teams, managing the dynamics across culture, AI use, and organizational...
View Details
Ransbotham, Sam, François Candelon, David Kiron, Burt LaFountain, and Shervin Khodabandeh. "The Cultural Benefits of Artificial Intelligence in the Enterprise." MIT Sloan Management Review, Big Ideas Artificial Intelligence and Business Strategy Initiative (website) (November 2, 2021). (Findings from the 2021 Artificial Intelligence and Business Strategy Global Executive Study and Research Project.)
- 20 Oct 2022 - 22 Oct 2022
- Talk
Stigma Against AI Companion Applications
By: Julian De Freitas, A. Ragnhildstveit and A.K. Uğuralp
- October 14, 2023
- Article
Will Consumers Buy Selfish Self-Driving Cars?
De Freitas, Julian. "Will Consumers Buy Selfish Self-Driving Cars?" Wall Street Journal (October 14, 2023), C5.
- September 23, 2024
- Article
AI Wants to Make You Less Lonely. Does It Work?
De Freitas, Julian. "AI Wants to Make You Less Lonely. Does It Work?" Wall Street Journal (September 23, 2024), R.11.
- 2023
- Working Paper
Distributionally Robust Causal Inference with Observational Data
By: Dimitris Bertsimas, Kosuke Imai and Michael Lingzhi Li
We consider the estimation of average treatment effects in observational studies and propose a new framework of robust causal inference with unobserved confounders. Our approach is based on distributionally robust optimization and proceeds in two steps. We first...
View Details
Bertsimas, Dimitris, Kosuke Imai, and Michael Lingzhi Li. "Distributionally Robust Causal Inference with Observational Data." Working Paper, February 2023.
- March 2024
- Exercise
'Storrowed': A Generative AI Exercise
By: Mitchell Weiss
"Storrowed" is an exercise to help participants raise their capacity and curiosity for generative AI. It focuses on generative AI for problem understanding and ideation, but can be adapted for use more broadly. Participants use generative AI tools to understand a...
View Details
Weiss, Mitchell. "'Storrowed': A Generative AI Exercise." Harvard Business School Exercise 824-188, March 2024.
- Winter 2021
- Editorial
Introduction
This issue of Negotiation Journal is dedicated to the theme of artificial intelligence, technology, and negotiation. It arose from a Program on Negotiation (PON) working conference on that important topic held virtually on May 17–18. The conference was not the...
View Details
Wheeler, Michael A. "Introduction." Special Issue on Artificial Intelligence, Technology, and Negotiation. Negotiation Journal 37, no. 1 (Winter 2021): 5–12.