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Show Results For
- All HBS Web
(2,852)
- People (14)
- News (651)
- Research (1,576)
- Events (19)
- Multimedia (9)
- Faculty Publications (845)
- Article
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness
By: Michael J Kearns, Seth Neel, Aaron Leon Roth and Zhiwei Steven Wu
The most prevalent notions of fairness in machine learning are statistical definitions: they fix a small collection of pre-defined groups, and then ask for parity of some statistic of the classifier (like classification rate or false positive rate) across these groups.... View Details
Kearns, Michael J., Seth Neel, Aaron Leon Roth, and Zhiwei Steven Wu. "Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness." Proceedings of the International Conference on Machine Learning (ICML) 35th (2018).
- December 2023
- Article
Self-Orienting in Human and Machine Learning
By: Julian De Freitas, Ahmet Uğuralp, Zeliha Uğuralp, Laurie Paul, Joshua B. Tenenbaum and T. Ullman
A current proposal for a computational notion of self is a representation of one’s body in a specific time and place, which includes the recognition of that representation as the agent. This turns self-representation into a process of self-orientation, a challenging... View Details
De Freitas, Julian, Ahmet Uğuralp, Zeliha Uğuralp, Laurie Paul, Joshua B. Tenenbaum, and T. Ullman. "Self-Orienting in Human and Machine Learning." Nature Human Behaviour 7, no. 12 (December 2023): 2126–2139.
- 06 Mar 2021
- News
How to Upgrade Judges with Machine Learning
- 21 Feb 2019
- Blog Post
Machine Learning and Behavioral Economics
This is a repost from the recruiting blog. For John Bracaglia, his academic and professional careers have been driven by two themes: “machine learning and behavioral economics,” he says. “The two work together. View Details
- 02–03 Dec 2022
- HBS Alumni Events
D^3 Catalyst: No Code Machine Learning and Artificial Intelligence
Do you want to delve into Machine Learning and Artificial Intelligence, but you feel overwhelmed and intimidated? Do you want to leverage the power of Machine Learning and Artificial Intelligence without writing any code? Do you want to leverage Machine Learning and... View Details
- 21 Nov 2015
- News
Machines Beat Humans at Hiring Best Employees
- October 2022 (Revised December 2022)
- Case
SMART: AI and Machine Learning for Wildlife Conservation
By: Brian Trelstad and Bonnie Yining Cao
Spatial Monitoring and Reporting Tool (SMART), a set of software and analytical tools designed for the purpose of wildlife conservation, had demonstrated significant improvements in patrol coverage, with some observed reductions in poaching and contributing to wildlife... View Details
Keywords: Business and Government Relations; Emerging Markets; Technology Adoption; Strategy; Management; Ethics; Social Enterprise; AI and Machine Learning; Analytics and Data Science; Natural Environment; Technology Industry; Cambodia; United States; Africa
Trelstad, Brian, and Bonnie Yining Cao. "SMART: AI and Machine Learning for Wildlife Conservation." Harvard Business School Case 323-036, October 2022. (Revised December 2022.)
- 26 Apr 2020
- Other Presentation
Towards Modeling the Variability of Human Attention
By: Kuno Kim, Megumi Sano, Julian De Freitas, Daniel Yamins and Nick Haber
Children exhibit extraordinary exploratory behaviors hypothesized to contribute to the building of models of their world. Harnessing this capacity in artificial systems promises not only more flexible technology but also cognitive models of the developmental processes... View Details
Keywords: Exploratory Learning Behaviors; Modeling; Artificial Intelligence; AI and Machine Learning
Kim, Kuno, Megumi Sano, Julian De Freitas, Daniel Yamins, and Nick Haber. "Towards Modeling the Variability of Human Attention." In Bridging AI and Cognitive Science (BAICS) Workshop. 8th International Conference on Learning Representations (ICLR), April 26, 2020.
- 25 Oct 2017
- Research & Ideas
Will Machine Learning Make You a Better Manager?
Credit: PhonlamaiPhoto Thirty years ago, the idea of a machine learning on its own would have stoked the worst kind of sci-fi nightmares about robots taking over the planet. These days, View Details
- November 2023
- Case
Open Source Machine Learning at Google
Set in early 2023, the case exposes students to the challenges of managing open source software at Google. The case focuses on the challenges for Alex Spinelli, Vice President of Product Management for Core Machine Learning. He must set priorities for Google’s efforts... View Details
Keywords: Decision Choices and Conditions; Technological Innovation; Open Source Distribution; Strategy; AI and Machine Learning; Applications and Software; Technology Industry; United States
Greenstein, Shane, Martin Wattenberg, Fernanda B. Viégas, Daniel Yue, and James Barnett. "Open Source Machine Learning at Google." Harvard Business School Case 624-015, November 2023.
- November 2021 (Revised December 2021)
- Supplement
PittaRosso (B): Human and Machine Learning
By: Ayelet Israeli
This case supplements the "PittaRosso: Artificial Intelligence-Driven Pricing and Promotion" case, and provides major highlights on what happened at the company since the first case. View Details
Keywords: Artificial Intelligence; Pricing; Pricing Algorithm; Pricing Decisions; Pricing Strategy; Pricing Structure; Promotion; Promotions; Online Marketing; Data-driven Decision-making; Data-driven Management; Retail; Retail Analytics; Price; Advertising Campaigns; Analytics and Data Science; Analysis; Digital Marketing; Budgets and Budgeting; Marketing Strategy; Marketing; Transformation; Decision Making; AI and Machine Learning; Retail Industry; Italy
Israeli, Ayelet. "PittaRosso (B): Human and Machine Learning." Harvard Business School Supplement 522-047, November 2021. (Revised December 2021.)
- 19 Jun 2020
- Podcast
Dexai: Machine learning in the kitchen
Advances in robotics have opened the way for the ultimate in smart kitchen appliances. Draper Labs spinoff, Dexai, makes the AI brains that coordinate the actions of Alfred, a robotic arm versatile enough follow recipes and handle orders in commercial kitchens.... View Details
- Research Summary
The Learning As BehaviorS (LABS) Model
The Learning As BehaviorS (LABS) Model of Expertise Development integrates research from management, cognitive psychology, educational psychology and neuroscience to describe the process of how a novice achieves expertise. Defining expertise as the ability to... View Details
- 2023
- Article
MoPe: Model Perturbation-based Privacy Attacks on Language Models
By: Marvin Li, Jason Wang, Jeffrey Wang and Seth Neel
Recent work has shown that Large Language Models (LLMs) can unintentionally leak sensitive information present in their training data. In this paper, we present Model Perturbations (MoPe), a new method to identify with high confidence if a given text is in the training... View Details
Li, Marvin, Jason Wang, Jeffrey Wang, and Seth Neel. "MoPe: Model Perturbation-based Privacy Attacks on Language Models." Proceedings of the Conference on Empirical Methods in Natural Language Processing (2023): 13647–13660.
- April 2025
- Background Note
Climate Change Adaptation with Artificial Intelligence and Machine Learning
By: Michael W. Toffel and Nabig Chaudhry
Artificial Intelligence (AI) and machine learning (ML) have emerged as powerful tools to address climate change. This note summarizes a wide range of the uses of AI/ML to drive climate change adaptation and resilience, the measures organizations and governments are... View Details
- November 2023 (Revised June 2024)
- Case
Zest AI: Machine Learning and Credit Access
By: David S. Scharfstein and Ryan Gilland
Scharfstein, David S., and Ryan Gilland. "Zest AI: Machine Learning and Credit Access." Harvard Business School Case 224-033, November 2023. (Revised June 2024.)
- 01 Nov 2018
- Working Paper Summaries
Forecasting Airport Transfer Passenger Flow Using Real-Time Data and Machine Learning
- 14 Mar 2023
- Cold Call Podcast
Can AI and Machine Learning Help Park Rangers Prevent Poaching?
- Article
Robust and Stable Black Box Explanations
By: Himabindu Lakkaraju, Nino Arsov and Osbert Bastani
As machine learning black boxes are increasingly being deployed in real-world applications, there
has been a growing interest in developing post hoc explanations that summarize the behaviors
of these black boxes. However, existing algorithms for generating such... View Details
Lakkaraju, Himabindu, Nino Arsov, and Osbert Bastani. "Robust and Stable Black Box Explanations." Proceedings of the International Conference on Machine Learning (ICML) 37th (2020): 5628–5638. (Published in PMLR, Vol. 119.)
- 21 Aug 2019
- Research & Ideas
What Machine Learning Teaches Us about CEO Leadership Style
CEOs are communicators. Studies show that CEOs spend 85 percent of their time in communication-related activities, including speeches, meetings, and phone calls with people both inside and outside the firm. Now, new research using machine... View Details
Keywords: by Michael Blanding