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Show Results For
- All HBS Web
(1,283)
- People (2)
- News (188)
- Research (814)
- Events (14)
- Multimedia (3)
- Faculty Publications (591)
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- 9 Dec 2016
- Conference Presentation
Learning Cost-Effective and Interpretable Regimes for Treatment Recommendation
By: Himabindu Lakkaraju and Cynthia Rudin
Lakkaraju, Himabindu, and Cynthia Rudin. "Learning Cost-Effective and Interpretable Regimes for Treatment Recommendation." Paper presented at the 30th Annual Conference on Neural Information Processing Systems (NIPS), Workshop on Interpretable Machine Learning in Complex Systems, Barcelona, Spain, December 9, 2016.
- 2022
- Working Paper
TalkToModel: Explaining Machine Learning Models with Interactive Natural Language Conversations
By: Dylan Slack, Satyapriya Krishna, Himabindu Lakkaraju and Sameer Singh
Practitioners increasingly use machine learning (ML) models, yet they have become more complex and harder to understand. To address this issue, researchers have proposed techniques to explain model predictions. However, practitioners struggle to use explainability... View Details
Slack, Dylan, Satyapriya Krishna, Himabindu Lakkaraju, and Sameer Singh. "TalkToModel: Explaining Machine Learning Models with Interactive Natural Language Conversations." Working Paper, 2022.
- Article
Faithful and Customizable Explanations of Black Box Models
By: Himabindu Lakkaraju, Ece Kamar, Rich Caruana and Jure Leskovec
As predictive models increasingly assist human experts (e.g., doctors) in day-to-day decision making, it is crucial for experts to be able to explore and understand how such models behave in different feature subspaces in order to know if and when to trust them. To... View Details
Lakkaraju, Himabindu, Ece Kamar, Rich Caruana, and Jure Leskovec. "Faithful and Customizable Explanations of Black Box Models." Proceedings of the AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (2019).
- Research Summary
Adoption of Machine Learning Models in Real World Decision Making
The goal of this research is to assess the impact of deploying machine learning models in real world decision making in domains such as health care. View Details
- August 2023
- Article
Explaining Machine Learning Models with Interactive Natural Language Conversations Using TalkToModel
By: Dylan Slack, Satyapriya Krishna, Himabindu Lakkaraju and Sameer Singh
Practitioners increasingly use machine learning (ML) models, yet models have become more complex and harder to understand. To understand complex models, researchers have proposed techniques to explain model predictions. However, practitioners struggle to use... View Details
Slack, Dylan, Satyapriya Krishna, Himabindu Lakkaraju, and Sameer Singh. "Explaining Machine Learning Models with Interactive Natural Language Conversations Using TalkToModel." Nature Machine Intelligence 5, no. 8 (August 2023): 873–883.
- 14 Mar 2023
- Cold Call Podcast
Can AI and Machine Learning Help Park Rangers Prevent Poaching?
- 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.)
- 8 Dec 2016
- Conference Presentation
Learning Cost-Effective and Interpretable Treatment Regimes for Judicial Bail Decisions
By: Himabindu Lakkaraju and Cynthia Rudin
Lakkaraju, Himabindu, and Cynthia Rudin. "Learning Cost-Effective and Interpretable Treatment Regimes for Judicial Bail Decisions." Paper presented at the 30th Annual Conference on Neural Information Processing Systems (NIPS), Symposium on Machine Learning and the Law, Barcelona, Spain, December 8, 2016.
- 01 Nov 2018
- Working Paper Summaries
Forecasting Airport Transfer Passenger Flow Using Real-Time Data and Machine Learning
- 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
- Working Paper
Visual Uniqueness in Peer-to-Peer Marketplaces: Machine Learning Model Development, Validation, and Application
By: Flora Feng, Charis Li and Shunyuan Zhang
Peer-to-peer (P2P) marketplaces have seen exponential growth in recent years featured by unique offerings from individual providers. Despite the perceived value of uniqueness, scalable quantification of visual uniqueness in P2P platforms like Airbnb has been largely... View Details
Keywords: Peer-to-peer Markets; Marketplace Matching; AI and Machine Learning; Demand and Consumers; Digital Platforms; Marketing
Feng, Flora, Charis Li, and Shunyuan Zhang. "Visual Uniqueness in Peer-to-Peer Marketplaces: Machine Learning Model Development, Validation, and Application." SSRN Working Paper Series, No. 4665286, February 2024.
- 2018
- Working Paper
Forecasting Airport Transfer Passenger Flow Using Real-Time Data and Machine Learning
By: Xiaojia Guo, Yael Grushka-Cockayne and Bert De Reyck
Problem definition: In collaboration with Heathrow Airport, we develop a predictive system that generates quantile forecasts of transfer passengers’ connection times. Sampling from the distribution of individual passengers’ connection times, the system also produces... View Details
Keywords: Quantile Forecasts; Regression Tree; Copula; Passenger Flow Management; Data-driven Operations; Forecasting and Prediction; Data and Data Sets
Guo, Xiaojia, Yael Grushka-Cockayne, and Bert De Reyck. "Forecasting Airport Transfer Passenger Flow Using Real-Time Data and Machine Learning." Harvard Business School Working Paper, No. 19-040, October 2018.
- Article
Productivity and Selection of Human Capital with Machine Learning
By: Aaron Chalfin, Oren Danieli, Andrew Hillis, Zubin Jelveh, Michael Luca, Jens Ludwig and Sendhil Mullainathan
Keywords: Analytics and Data Science; Selection and Staffing; Performance Productivity; Mathematical Methods; Policy
Chalfin, Aaron, Oren Danieli, Andrew Hillis, Zubin Jelveh, Michael Luca, Jens Ludwig, and Sendhil Mullainathan. "Productivity and Selection of Human Capital with Machine Learning." American Economic Review: Papers and Proceedings 106, no. 5 (May 2016): 124–127.
- 04 Oct 2019
- Working Paper Summaries
Soul and Machine (Learning)
- Research Summary
Overview
Jenny is broadly interested in interpretable machine learning (ML), identity and inequality, and improving existing methods used to answer social and policy-relevant questions. Her recent projects have focused on developing tools that explore how LLMs are reshaping... View Details
- April 2018 (Revised February 2019)
- Supplement
Improving Worker Safety in the Era of Machine Learning (B)
By: Michael W. Toffel, Dan Levy, Astrid Camille Pineda, Jose Ramon Morales Arilla and Matthew S. Johnson
Supplements the (A) case. View Details
Toffel, Michael W., Dan Levy, Astrid Camille Pineda, Jose Ramon Morales Arilla, and Matthew S. Johnson. "Improving Worker Safety in the Era of Machine Learning (B)." Harvard Business School Supplement 618-064, April 2018. (Revised February 2019.)
- Article
When Dreaming Is Believing: The (Motivated) Interpretation of Dreams
By: Carey K. Morewedge and Michael I. Norton
This research investigated laypeople's interpretation of their dreams. Participants from both Eastern and Western cultures believed that dreams contain hidden truths (Study 1) and considered dreams to provide more meaningful information about the world than similar... View Details
Keywords: Anchoring; Attribution; Dreams; Motivated Reasoning; Unconscious Thought; Communication Intention and Meaning; Judgments; Values and Beliefs; Information; Behavior; Cognition and Thinking; Motivation and Incentives
Morewedge, Carey K., and Michael I. Norton. "When Dreaming Is Believing: The (Motivated) Interpretation of Dreams." Journal of Personality and Social Psychology 96, no. 2 (February 2009): 249–264. (Winner of Society for Personality and Social Psychology. Theoretical Innovation Prize For an article or book chapter judged to provide the most innovative theoretical contribution to social/personality psychology within a given year presented by Society for Personality and Social Psychology.)
- 2023
- Chapter
Marketing Through the Machine’s Eyes: Image Analytics and Interpretability
By: Shunyuan Zhang, Flora Feng and Kannan Srinivasan
he growth of social media and the sharing economy is generating abundant unstructured image and video data. Computer vision techniques can derive rich insights from unstructured data and can inform recommendations for increasing profits and consumer utility—if only the... View Details
Zhang, Shunyuan, Flora Feng, and Kannan Srinivasan. "Marketing Through the Machine’s Eyes: Image Analytics and Interpretability." Chap. 8 in Artificial Intelligence in Marketing. 20, edited by Naresh K. Malhotra, K. Sudhir, and Olivier Toubia, 217–238. Review of Marketing Research. Emerald Publishing Limited, 2023.