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- All HBS Web
(926)
- Faculty Publications (352)
- July 2016
- Case
Spotify
By: Anita Elberse and Alexandre de Pfyffer
In November 2014, Spotify's chief content officer Ken Parks learns that record label Big Machine Records has requested the immediate removal of superstar artist Taylor Swift's entire catalogue from Spotify's music streaming service. Is it time for Spotify to reconsider... View Details
Keywords: Entertainment; Marketing; Superstar; Music; Entertainment Marketing; Media; Digital Technology; Creative Industries; Product Portfolio Management; General Management; Management; Strategy; Internet and the Web; Open Source Distribution; Creativity; Music Entertainment; Product Marketing; Music Industry
Elberse, Anita, and Alexandre de Pfyffer. "Spotify." Harvard Business School Case 516-046, July 2016.
- June 2016 (Revised August 2019)
- Case
Numenta: Inventing and (or) Commercializing AI
By: David B. Yoffie, Liz Kind and David Ben Shimol
In March 2016, Donna Dubinsky (co-founder and CEO) and Jeff Hawkins (co-founder) were struggling with a key question: Could Numenta be successful in both creating fundamental technology and building a commercial business? Located in Redwood City, CA, Numenta was... View Details
Keywords: Artificial Intelligence; Machine Intelligence; Machine Learning; Strategy; Business Model; Entrepreneurship; Information; Technological Innovation; Research; Research and Development; Information Technology; Applications and Software; Technology Adoption; Digital Platforms; Commercialization; AI and Machine Learning
Yoffie, David B., Liz Kind, and David Ben Shimol. "Numenta: Inventing and (or) Commercializing AI." Harvard Business School Case 716-469, June 2016. (Revised August 2019.)
- Article
Crowdsourcing City Government: Using Tournaments to Improve Inspection Accuracy
By: Edward Glaeser, Andrew Hillis, Scott Duke Kominers and Michael Luca
The proliferation of big data makes it possible to better target city services like hygiene inspections, but city governments rarely have the in-house talent needed for developing prediction algorithms. Cities could hire consultants, but a cheaper alternative is to... View Details
Keywords: User-generated Content; Operations; Tournaments; Policy-making; Machine Learning; Online Platforms; Analytics and Data Science; Mathematical Methods; City; Infrastructure; Business Processes; Government and Politics
Glaeser, Edward, Andrew Hillis, Scott Duke Kominers, and Michael Luca. "Crowdsourcing City Government: Using Tournaments to Improve Inspection Accuracy." American Economic Review: Papers and Proceedings 106, no. 5 (May 2016): 114–118.
- Winter 2016
- Article
Analytics for an Online Retailer: Demand Forecasting and Price Optimization
By: Kris J. Ferreira, Bin Hong Alex Lee and David Simchi-Levi
We present our work with an online retailer, Rue La La, as an example of how a retailer can use its wealth of data to optimize pricing decisions on a daily basis. Rue La La is in the online fashion sample sales industry, where they offer extremely limited-time... View Details
Ferreira, Kris J., Bin Hong Alex Lee, and David Simchi-Levi. "Analytics for an Online Retailer: Demand Forecasting and Price Optimization." Manufacturing & Service Operations Management 18, no. 1 (Winter 2016): 69–88.
- November 2015 (Revised May 2016)
- Case
Aspiring Minds
By: Karim R. Lakhani, Marco Iansiti and Christine Snively
By 2015, India-based employment assessment and certification provider Aspiring Minds had helped facilitate over 300,000 job matches through its assessment tools. Aspiring Minds' flagship product, the Aspiring Minds Computer Adaptive Test (AMCAT), used machine learning... View Details
Keywords: Information Technology; Strategy; Higher Education; Technological Innovation; Employment; Technology Industry; India; China
Lakhani, Karim R., Marco Iansiti, and Christine Snively. "Aspiring Minds." Harvard Business School Case 616-013, November 2015. (Revised May 2016.)
- 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... View Details
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.)
- 2015
- Article
A Machine Learning Framework to Identify Students at Risk of Adverse Academic Outcomes
By: Himabindu Lakkaraju, Everaldo Aguiar, Carl Shan, David Miller, Nasir Bhanpuri, Rayid Ghani and Kecia Addison
Lakkaraju, Himabindu, Everaldo Aguiar, Carl Shan, David Miller, Nasir Bhanpuri, Rayid Ghani, and Kecia Addison. "A Machine Learning Framework to Identify Students at Risk of Adverse Academic Outcomes." Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining 21st (2015).
- 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).
- December 1984
- Case
Expense Tracking System at Tiger Creek
By: Shoshana Zuboff
Mill manager Carl Adelman learns that a group of senior managers is soon to visit the Tiger Creek mill to learn more about the success of the newly implemented Expense Tracking System. The System had been installed on two paper machines to give workers real time cost... View Details
Zuboff, Shoshana. "Expense Tracking System at Tiger Creek." Harvard Business School Case 485-057, December 1984.
- 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
- Forthcoming
- Article
Disclosure, Humanizing, and Contextual Vulnerability of Generative AI Chatbots
By: Julian De Freitas and I. Glenn Cohen
In the wake of recent advancements in generative AI, regulatory bodies are trying to keep pace. One key decision is whether to require app makers to disclose the use of generative AI-powered chatbots in their products. We suggest that some generative AI-based chatbots... View Details
- Teaching Interest
Empirical Technology and Operations Management Course
I taught a set of lectures on "Introduction to Machine Learning for Social Scientists" as part of this required course for first year PhD students. This module familiarizes students with all the basic concepts in machine learning, their implementations, as well as the... View Details
- Teaching Interest
Harvard Business Analytics Program: Operations and Supply Chain Management
By: Dennis Campbell
Digital technologies and data analytics are radically changing the operating model of an organization and how it connects to its broader supply chain and ecosystem. This course emphasizes managing product availability, especially in a context of rapid product... View Details
- Forthcoming
- Article
Human-Algorithm Collaboration with Private Information: Naïve Advice Weighting Behavior and Mitigation
By: Maya Balakrishnan, Kris Ferreira and Jordan Tong
Even if algorithms make better predictions than humans on average, humans may sometimes have private information which an algorithm does not have access to that can improve performance. How can we help humans effectively use and adjust recommendations made by... View Details
Keywords: AI and Machine Learning; Analytics and Data Science; Forecasting and Prediction; Digital Marketing
Balakrishnan, Maya, Kris Ferreira, and Jordan Tong. "Human-Algorithm Collaboration with Private Information: Naïve Advice Weighting Behavior and Mitigation." Management Science (forthcoming).
- Teaching Interest
Interpretability and Explainability in Machine Learning
As machine learning models are increasingly being employed to aid decision makers in high-stakes settings such as healthcare and criminal justice, it is important to ensure that the decision makers correctly understand and consequent trust the functionality of these... View Details
- Research Summary
Making Machine Learning Models Fair
The goal of this research direction is to ensure that the machine learning models we build and deploy do not discriminate against individuals from minority groups. View Details
- Research Summary
Making Machine Learning Models Interpretable
I work on developing various tools and methodologies which can help decision makers (e.g., doctors, managers) to better understand the predictions of machine learning models. View Details
- Research Summary
Making Machine Learning Robust to Adversarial Attacks
The goal of this research is to ensure that machine learning models that we build and deploy are not easily susceptible to attacks by adversarial or malicious entities. View Details
- 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. Review of Marketing Research. Emerald Publishing Limited, forthcoming.
- Forthcoming
- Article
Outcome-Driven Dynamic Refugee Assignment with Allocation Balancing
By: Kirk Bansak and Elisabeth Paulson
This study proposes two new dynamic assignment algorithms to match refugees and asylum seekers to geographic localities within a host country. The first, currently implemented in a multi-year pilot in Switzerland, seeks to maximize the average predicted employment... View Details
Bansak, Kirk, and Elisabeth Paulson. "Outcome-Driven Dynamic Refugee Assignment with Allocation Balancing." Operations Research (forthcoming). (Pre-published online March 25, 2024.)