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Show Results For
-
All HBS Web
(808)
- People (1)
- News (147)
- Research (498)
- Events (7)
- Multimedia (4)
- Faculty Publications (403)
- Article
Counterfactual Explanations Can Be Manipulated
By: Dylan Slack, Sophie Hilgard, Himabindu Lakkaraju and Sameer Singh
Counterfactual explanations are useful for both generating recourse and auditing fairness between groups. We seek to understand whether adversaries can manipulate counterfactual explanations in an algorithmic recourse setting: if counterfactual explanations indicate...
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Slack, Dylan, Sophie Hilgard, Himabindu Lakkaraju, and Sameer Singh. "Counterfactual Explanations Can Be Manipulated." Advances in Neural Information Processing Systems (NeurIPS) 34 (2021).
- November 2022
- Article
A Language-Based Method for Assessing Symbolic Boundary Maintenance between Social Groups
By: Anjali M. Bhatt, Amir Goldberg and Sameer B. Srivastava
When the social boundaries between groups are breached, the tendency for people to erect and maintain symbolic boundaries intensifies. Drawing on extant perspectives on boundary maintenance, we distinguish between two strategies that people pursue in maintaining...
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Keywords:
Culture;
Machine Learning;
Natural Language Processing;
Symbolic Boundaries;
Organizations;
Boundaries;
Social Psychology;
Interpersonal Communication;
Organizational Culture
Bhatt, Anjali M., Amir Goldberg, and Sameer B. Srivastava. "A Language-Based Method for Assessing Symbolic Boundary Maintenance between Social Groups." Sociological Methods & Research 51, no. 4 (November 2022): 1681–1720.
- October–December 2022
- Article
Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem
By: Mochen Yang, Edward McFowland III, Gordon Burtch and Gediminas Adomavicius
Combining machine learning with econometric analysis is becoming increasingly prevalent in both research and practice. A common empirical strategy involves the application of predictive modeling techniques to "mine" variables of interest from available data, followed...
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Keywords:
Machine Learning;
Econometric Analysis;
Instrumental Variable;
Random Forest;
Causal Inference;
AI and Machine Learning;
Forecasting and Prediction
Yang, Mochen, Edward McFowland III, Gordon Burtch, and Gediminas Adomavicius. "Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem." INFORMS Journal on Data Science 1, no. 2 (October–December 2022): 138–155.
- December 2020
- Supplement
VIA Science (B)
By: Juan Alcácer, Rembrand Koning, Annelena Lobb and Kerry Herman
Via (a) captures the early days of the data analytics startup as founders Gounden and Ravanis considered which markets offer the right opportunities for their firm and what kinds of experiments will help them narrow their choice. Supplement Via (b) reveals the...
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Keywords:
Data Analytics;
Machine Learning;
Artificial Intelligence;
Strategy;
Business Startups;
AI and Machine Learning;
Telecommunications Industry;
Utilities Industry;
United States;
Japan
Alcácer, Juan, Rembrand Koning, Annelena Lobb, and Kerry Herman. "VIA Science (B)." Harvard Business School Supplement 721-368, December 2020.
- Article
Who, When, and Why: A Machine Learning Approach to Prioritizing Students at Risk of Not Graduating High School on Time
By: Everaldo Aguiar, Himabindu Lakkaraju, Nasir Bhanpuri, David Miller, Ben Yuhas, Kecia Addison and Rayid Ghani
Aguiar, Everaldo, Himabindu Lakkaraju, Nasir Bhanpuri, David Miller, Ben Yuhas, Kecia Addison, and Rayid Ghani. "Who, When, and Why: A Machine Learning Approach to Prioritizing Students at Risk of Not Graduating High School on Time." Proceedings of the International Learning Analytics and Knowledge Conference 5th (2015).
- July 2019 (Revised November 2019)
- Case
Osaro: Picking the Best Path
By: William R. Kerr, James Palano and Bastiane Huang
The founder of Osaro saw the potential of deep reinforcement learning to allow robots to be applied to new applications. Osaro targeted warehousing, already a dynamic industry for robotics and automation, for its initial product—a system which would allow robotic arms...
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Keywords:
Artificial Intelligence;
Machine Learning;
Robotics;
Robots;
Ecommerce;
Fulfillment;
Warehousing;
AI;
Startup;
Technology Commercialization;
Business Startups;
Entrepreneurship;
Logistics;
Order Taking and Fulfillment;
Information Technology;
Commercialization;
Learning;
Complexity;
Competition;
E-commerce
Kerr, William R., James Palano, and Bastiane Huang. "Osaro: Picking the Best Path." Harvard Business School Case 820-012, July 2019. (Revised November 2019.)
- October 2023
- Case
ReUp Education: Can AI Help Learners Return to College?
By: Kris Ferreira, Christopher Thomas Ryan and Sarah Mehta
Founded in 2015, ReUp Education helps “stopped out students”—learners who have stopped making progress towards graduation—achieve their college completion goals. The company relies on a team of success coaches to engage with learners and help them reenroll. In 2019,...
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Keywords:
AI;
Algorithms;
Machine Learning;
Edtech;
Education Technology;
Analysis;
Higher Education;
AI and Machine Learning;
Customization and Personalization;
Failure;
Education Industry;
Technology Industry;
United States
Ferreira, Kris, Christopher Thomas Ryan, and Sarah Mehta. "ReUp Education: Can AI Help Learners Return to College?" Harvard Business School Case 624-007, October 2023.
- 2017
- Working Paper
The Need for Speed: Effects of Uncertainty Reduction in Patenting
By: Mike Horia Teodorescu
Patents are essential in commerce to establish property rights for ideas and to give equal protection to firms that develop new technologies. Young firms especially depend on the protection of intellectual property to bring a product from concept to market. However,...
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- 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...
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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).
- December 2020
- Case
VIA Science (A)
By: Juan Alcácer, Rembrand Koning, Annelena Lobb and Kerry Herman
Via (a) captures the early days of the data analytics startup as founders Gounden and Ravanis considered which markets offer the right opportunities for their firm and what kinds of experiments will help them narrow their choice. Supplement Via (b) reveals the...
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Keywords:
Data Analytics;
Machine Learning;
Artificial Intelligence;
Strategy;
Business Startups;
Markets;
AI and Machine Learning;
Telecommunications Industry;
Utilities Industry;
United States;
Japan
Alcácer, Juan, Rembrand Koning, Annelena Lobb, and Kerry Herman. "VIA Science (A)." Harvard Business School Case 721-367, December 2020.
- January 2018 (Revised February 2023)
- Teaching Note
The Future of Patent Examination at the USPTO
This teaching note pairs with the case entitled: “The Future of Patent Examination at the USPTO” (case no. 617-027).
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- October 2015 (Revised October 2016)
- Case
Building Watson: Not So Elementary, My Dear! (Abridged)
By: Willy C. Shih
This case is set inside IBM Research's efforts to build a computer that can successfully take on human challengers playing the game show Jeopardy! It opens with the machine named Watson offering the incorrect answer "Toronto" to a seemingly simple question during the...
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Keywords:
Analytics;
Big Data;
Business Analytics;
Product Development Strategy;
Machine Learning;
Machine Intelligence;
Artificial Intelligence;
Product Development;
AI and Machine Learning;
Information Technology;
Analytics and Data Science;
Information Technology Industry;
United States
Shih, Willy C. "Building Watson: Not So Elementary, My Dear! (Abridged)." Harvard Business School Case 616-025, October 2015. (Revised October 2016.)
- September 2020 (Revised March 2022)
- Case
JOANN: Joannalytics Inventory Allocation Tool
By: Kris Ferreira and Srikanth Jagabathula
Michael Joyce, Vice President of Inventory Management at JOANN, championed an effort to develop and implement an inventory allocation analytics tool that used advanced analytics to predict in-season demand of seasonal items for each of JOANN’s nearly 900 stores and...
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Keywords:
Analytics;
Machine Learning;
Optimization;
Inventory Management;
Mathematical Methods;
Decision Making;
Operations;
Supply Chain Management;
Resource Allocation;
Distribution;
Technology Adoption;
Applications and Software;
Change Management;
Fashion Industry;
Consumer Products Industry;
Retail Industry;
United States;
Ohio
Ferreira, Kris, and Srikanth Jagabathula. "JOANN: Joannalytics Inventory Allocation Tool." Harvard Business School Case 621-055, September 2020. (Revised March 2022.)
- 2020
- Book
Work, Mate, Marry, Love: How Machines Shape Our Human Destiny
By: Debora L. Spar
Covering a time frame that ranges from 8000 BC to the present, and drawing upon both Marxist and feminist theories, the book argues that nearly all the decisions we make in our most intimate lives—whom to marry, how to have children, how to have sex, how to think about...
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Keywords:
Innovation;
Family;
Women;
Reproduction;
Artificial Intelligence;
Robots;
Gender;
Demography;
History;
Innovation and Invention;
Relationships;
Society;
Information Technology;
AI and Machine Learning;
Biotechnology Industry;
Computer Industry;
Health Industry;
Information Technology Industry;
Manufacturing Industry;
Technology Industry;
Africa;
Asia;
Europe;
Latin America;
North and Central America
Spar, Debora L. Work, Mate, Marry, Love: How Machines Shape Our Human Destiny. New York: Farrar, Straus and Giroux, 2020.
- October 20, 2020
- Article
Expanding AI's Impact with Organizational Learning
By: Sam Ransbotham, Shervin Khodabandeh, David Kiron, François Candelon, Michael Chu and Burt LaFountain
Most companies developing AI capabilities have yet to gain significant financial benefits from their efforts. Only when organizations add the ability to learn with AI do significant benefits become likely.
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Ransbotham, Sam, Shervin Khodabandeh, David Kiron, François Candelon, Michael Chu, and Burt LaFountain. "Expanding AI's Impact with Organizational Learning." MIT Sloan Management Review, Big Ideas Artificial Intelligence and Business Strategy Initiative (website) (October 20, 2020). (Findings from the 2020 Artificial Intelligence
Global Executive Study and Research Project.)
- 2021
- Working Paper
First Law of Motion: Influencer Video Advertising on TikTok
By: Jeremy Yang, Juanjuan Zhang and Yuhan Zhang
This paper engineers an intuitive feature that is predictive of the causal effect of influencer video advertising on product sales. We propose the concept of m-score, a summary statistic that captures the extent to which a product is advertised in the most engaging...
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Keywords:
Influencer Advertising;
Video Advertising;
Computer Vision;
Machine Learning;
Advertising;
Online Technology
Yang, Jeremy, Juanjuan Zhang, and Yuhan Zhang. "First Law of Motion: Influencer Video Advertising on TikTok." Working Paper, March 2021.
- 29 May 2013
- Blog Post
Reflections and learnings
So, without further ado, the 7* lessons that made this all worthwhile for me: Winning is not easy: It’s easy to look at an industry leader like Sephora and imagine a seamlessly polished machine cranking out innovation after the next. Not...
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Keywords:
Consumer Products / Retail
- February 2018
- Article
Retention Futility: Targeting High-Risk Customers Might Be Ineffective.
By: Eva Ascarza
Companies in a variety of sectors are increasingly managing customer churn proactively, generally by detecting customers at the highest risk of churning and targeting retention efforts towards them. While there is a vast literature on developing churn prediction models...
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Keywords:
Retention/churn;
Proactive Churn Management;
Field Experiments;
Heterogeneous Treatment Effect;
Machine Learning;
Customer Relationship Management;
Risk Management
Ascarza, Eva. "Retention Futility: Targeting High-Risk Customers Might Be Ineffective." Journal of Marketing Research (JMR) 55, no. 1 (February 2018): 80–98.
- Article
Algorithms Need Managers, Too
By: Michael Luca, Jon Kleinberg and Sendhil Mullainathan
Algorithms are powerful predictive tools, but they can run amok when not applied properly. Consider what often happens with social media sites. Today many use algorithms to decide which ads and links to show users. But when these algorithms focus too narrowly on...
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Keywords:
Machine Learning;
Algorithms;
Predictive Analytics;
Management;
Big Data;
Analytics and Data Science
Luca, Michael, Jon Kleinberg, and Sendhil Mullainathan. "Algorithms Need Managers, Too." Harvard Business Review 94, nos. 1/2 (January–February 2016): 96–101.
- July 2019
- Teaching Note
Miroglio Fashion
By: Sunil Gupta
Teaching Note for HBS Nos. 519-053, 519-070, and 519-072.
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