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(978)
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- News (187)
- Research (622)
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- Faculty Publications (498)
Show Results For
- All HBS Web
(978)
- People (1)
- News (187)
- Research (622)
- Events (11)
- Multimedia (3)
- Faculty Publications (498)
- 28 Mar 2017
- Working Paper Summaries
CEO Behavior and Firm Performance
- 17 Jun 2021
- News
Too Few Women Get to Invent – That’s a Problem for Women’s Health
- December 2020 (Revised April 2021)
- Case
IBM Watson at MD Anderson Cancer Center
By: Shane Greenstein, Mel Martin and Sarkis Agaian
After discovering that their cancer diagnostic tool, designed to leverage the cloud computing power of IBM Watson, needed greater integration into the clinical processes at the MD Anderson Cancer Center, the development team had difficult choices to make. The Oncology... View Details
Keywords: Decision Making; Innovation Strategy; Knowledge Management; Knowledge Use and Leverage; Operations; Failure; Information Technology; Applications and Software; Health Care and Treatment; Product Development; Health Industry; Information Technology Industry; Technology Industry; United States; Houston; Texas
Greenstein, Shane, Mel Martin, and Sarkis Agaian. "IBM Watson at MD Anderson Cancer Center." Harvard Business School Case 621-022, December 2020. (Revised April 2021.)
Jenny Wang
Jenny Shan Wang is a doctoral student in the Technology and Operations Management program at Harvard Business School (HBS). She is broadly interested in interpretable machine learning (ML), identity and inequality, and improving existing methods... View Details
- 20 Sep 2014
- News
Making Big Data Think Bigger
- January 2019 (Revised October 2019)
- Case
Liulishuo: AI English Teacher
By: John J-H Kim and Shu Lin
Educators and entrepreneurs alike are excited about the potential for artificial intelligence (AI) and machine learning to change the way learning will look like in the future. There is a confluence of factors such as the availability of large sources of rich,... View Details
Keywords: AI; Artificial Intelligence; Education Technology; Information Technology; Education; Entrepreneurship; AI and Machine Learning; Education Industry; China
Kim, John J-H, and Shu Lin. "Liulishuo: AI English Teacher." Harvard Business School Case 319-090, January 2019. (Revised October 2019.)
- Teaching Interest
Overview
Paul is primarily interested in teaching data science to management students through the case method. This includes technical topics (programming and statistics) as well as higher-level management issues (digital transformation, data governance, etc.) As a research... View Details
Keywords: A/B Testing; AI; AI Algorithms; AI Creativity; Algorithm; Algorithm Bias; Algorithmic Bias; Algorithmic Fairness; Algorithms; Analytics; Application Program Interface; Artificial Intelligence; Causality; Causal Inference; Computing; Computers; Data Analysis; Data Analytics; Data Architecture; Data As A Service; Data Centers; Data Governance; Data Labeling; Data Management; Data Manipulation; Data Mining; Data Ownership; Data Privacy; Data Protection; Data Science; Data Science And Analytics Management; Data Scientists; Data Security; Data Sharing; Data Strategy; Data Visualization; Database; Data-driven Decision-making; Data-driven Management; Data-driven Operations; Datathon; Economics Of AI; Economics Of Innovation; Economics Of Information System; Economics Of Science; Forecast; Forecast Accuracy; Forecasting; Forecasting And Prediction; Information Technology; Machine Learning; Machine Learning Models; Prediction; Prediction Error; Predictive Analytics; Predictive Models; Analysis; AI and Machine Learning; Analytics and Data Science; Applications and Software; Digital Transformation; Information Management; Digital Strategy; Technology Adoption
- 2022
- Working Paper
Rethinking Explainability as a Dialogue: A Practitioner's Perspective
By: Himabindu Lakkaraju, Dylan Slack, Yuxin Chen, Chenhao Tan and Sameer Singh
As practitioners increasingly deploy machine learning models in critical domains such as healthcare, finance, and policy, it becomes vital to ensure that domain experts function effectively alongside these models. Explainability is one way to bridge the gap between... View Details
Keywords: Natural Language Conversations; AI and Machine Learning; Experience and Expertise; Interactive Communication; Business and Stakeholder Relations
Lakkaraju, Himabindu, Dylan Slack, Yuxin Chen, Chenhao Tan, and Sameer Singh. "Rethinking Explainability as a Dialogue: A Practitioner's Perspective." Working Paper, 2022.
- 19 Oct 2021
- HBS Seminar
Cynthia Rudin, Duke University
- 2023
- Article
Provable Detection of Propagating Sampling Bias in Prediction Models
By: Pavan Ravishankar, Qingyu Mo, Edward McFowland III and Daniel B. Neill
With an increased focus on incorporating fairness in machine learning models, it becomes imperative not only to assess and mitigate bias at each stage of the machine learning pipeline but also to understand the downstream impacts of bias across stages. Here we consider... View Details
Ravishankar, Pavan, Qingyu Mo, Edward McFowland III, and Daniel B. Neill. "Provable Detection of Propagating Sampling Bias in Prediction Models." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (2023): 9562–9569. (Presented at the 37th AAAI Conference on Artificial Intelligence (2/7/23-2/14/23) in Washington, DC.)
- 08 Mar 2017
- HBS Seminar
Fernanda Viégas and Martin Wattenberg, Google
- 19 Jan 2023
- Research & Ideas
What Makes Employees Trust (vs. Second-Guess) AI?
products were grouped in 241 “style-colors'' and sizes. When the allocators received a recommendation from an interpretable algorithm, they often overruled it based on their own intuition. But when the same allocators had a recommendation from a similarly accurate... View Details
Keywords: by Rachel Layne
- June 2019
- Teaching Note
Zebra Medical Vision
By: Shane Greenstein and Sarah Gulick
Teaching note is meant to accompany Zebra Medical Vision case, which offers a look at a company’s decisions as a small startup competing with other startups and major technology companies. It also demonstrates the challenges faced by a machine learning company working... View Details
- March–April 2023
- Article
Pricing for Heterogeneous Products: Analytics for Ticket Reselling
By: Michael Alley, Max Biggs, Rim Hariss, Charles Herrmann, Michael Lingzhi Li and Georgia Perakis
Problem definition: We present a data-driven study of the secondary ticket market. In particular, we are primarily concerned with accurately estimating price sensitivity for listed tickets. In this setting, there are many issues including endogeneity, heterogeneity in... View Details
Keywords: Price; Demand and Consumers; AI and Machine Learning; Investment Return; Entertainment and Recreation Industry; Sports Industry
Alley, Michael, Max Biggs, Rim Hariss, Charles Herrmann, Michael Lingzhi Li, and Georgia Perakis. "Pricing for Heterogeneous Products: Analytics for Ticket Reselling." Manufacturing & Service Operations Management 25, no. 2 (March–April 2023): 409–426.
Siyu Zhang
Siyu Zhang is a second-year doctoral student at HBS. Zhang joined Harvard Business School in 2020 as a Research Associate and has been working on macroeconomic forecasting projects. Prior to joining HBS, he was a Data Scientist at John Hancock, where he utilized... View Details
- 05 Jul 2023
- Cold Call Podcast
How Unilever Is Preparing for the Future of Work
- 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.
- 2023
- Working Paper
Nailing Prediction: Experimental Evidence on the Value of Tools in Predictive Model Development
By: Daniel Yue, Paul Hamilton and Iavor Bojinov
Predictive model development is understudied despite its centrality in modern artificial
intelligence and machine learning business applications. Although prior discussions
highlight advances in methods (along the dimensions of data, computing power, and
algorithms)... View Details
Keywords: Analytics and Data Science
Yue, Daniel, Paul Hamilton, and Iavor Bojinov. "Nailing Prediction: Experimental Evidence on the Value of Tools in Predictive Model Development." Harvard Business School Working Paper, No. 23-029, December 2022. (Revised April 2023.)