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- Faculty Publications (382)
- 2019
- Working Paper
Soul and Machine (Learning)
By: Davide Proserpio, John R. Hauser, Xiao Liu, Tomomichi Amano, Alex Burnap, Tong Guo, Dokyun Lee, Randall Lewis, Kanishka Misra, Eric Schwarz, Artem Timoshenko, Lilei Xu and Hema Yoganarasimhan
Machine learning is bringing us self-driving cars, improved medical diagnostics, and machine translation, but can it improve marketing decisions? It can. Machine learning models predict extremely well, are scalable to “big data,” and are a natural fit to rich media... View Details
Proserpio, Davide, John R. Hauser, Xiao Liu, Tomomichi Amano, Alex Burnap, Tong Guo, Dokyun Lee, Randall Lewis, Kanishka Misra, Eric Schwarz, Artem Timoshenko, Lilei Xu, and Hema Yoganarasimhan. "Soul and Machine (Learning)." Harvard Business School Working Paper, No. 20-036, September 2019.
- 2020
- Working Paper
Improving Regulatory Effectiveness Through Better Targeting: Evidence from OSHA
By: Matthew S. Johnson, David I. Levine and Michael W. Toffel
We study how a regulator can best target inspections. Our case study is a US Occupational Safety and Health Administration (OSHA) program that randomly allocated some inspections. On average, each inspection averted 2.4 serious injuries (9%) over the next five years.... View Details
Keywords: Government Administration; Working Conditions; Safety; Quality; Production; Analysis; Resource Allocation; Manufacturing Industry; United States
Johnson, Matthew S., David I. Levine, and Michael W. Toffel. "Improving Regulatory Effectiveness Through Better Targeting: Evidence from OSHA." Harvard Business School Working Paper, No. 20-019, August 2019. (Revised February 2020.)
- 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... View Details
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.)
- July 2019
- Teaching Note
Miroglio Fashion
By: Sunil Gupta
Teaching Note for HBS Nos. 519-053, 519-070, and 519-072. View Details
- 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
- Article
Use of Crowd Innovation to Develop an Artificial Intelligence-Based Solution for Radiation Therapy Targeting
By: Raymond H. Mak, Michael G. Endres, Jin Hyun Paik, Rinat A. Sergeev, Hugo Aerts, Christopher L. Williams, Karim R. Lakhani and Eva C. Guinan
Importance: Radiation therapy (RT) is a critical cancer treatment, but the existing radiation oncologist work force does not meet growing global demand. One key physician task in RT planning involves tumor segmentation for targeting, which requires substantial... View Details
Keywords: Crowdsourcing; AI Algorithms; Health Care and Treatment; Collaborative Innovation and Invention; AI and Machine Learning
Mak, Raymond H., Michael G. Endres, Jin Hyun Paik, Rinat A. Sergeev, Hugo Aerts, Christopher L. Williams, Karim R. Lakhani, and Eva C. Guinan. "Use of Crowd Innovation to Develop an Artificial Intelligence-Based Solution for Radiation Therapy Targeting." JAMA Oncology 5, no. 5 (May 2019): 654–661.
- April 2019 (Revised June 2019)
- Case
From Globalization to Dual Digital Transformation: CEO Thierry Breton Leading Atos Into 'Digital Shockwaves' (A)
By: Tsedal Neeley, JT Keller and James Barnett
Thierry Breton, chairman and CEO of IT company Atos, faced a pivotal juncture. After spending eight intense years scaling the company globally to over 100,000 employees in 70 countries, he was ready to take the next crucial step. Breton was convinced that rapid digital... View Details
Keywords: Dual Digital Transformation; Transformation; Disruption; Employees; Competency and Skills; Training; Decision Making; Digital Transformation
Neeley, Tsedal, JT Keller, and James Barnett. "From Globalization to Dual Digital Transformation: CEO Thierry Breton Leading Atos Into 'Digital Shockwaves' (A)." Harvard Business School Case 419-027, April 2019. (Revised June 2019.)
- 2019
- Book
Fintech, Small Business & the American Dream: How Technology Is Transforming Lending and Shaping a New Era of Small Business Opportunity
By: Karen G. Mills
Fintech, Small Business & the American Dream describes the needs of small businesses for capital and demonstrates how technology—novel data sources, artificial intelligence, machine learning—will transform the small business lending market. This market has been... View Details
Keywords: Fintech; Big Data; Data; Technology; Artificial Intelligence; Great Recession; Regulation; Innovation; Banks; Lending; Loans; Access To Capital; American Dream; Community Banking; Small Business Administration; Entrepreneur; Government; Public Policy; API; Policy Making; Small Business; Financing and Loans; Technological Innovation; Financial Crisis; Banks and Banking; Governing Rules, Regulations, and Reforms; Policy; AI and Machine Learning; Analytics and Data Science; United States
Mills, Karen G. Fintech, Small Business & the American Dream: How Technology Is Transforming Lending and Shaping a New Era of Small Business Opportunity. Palgrave Macmillan, 2019.
- March 2019
- Teaching Note
Numenta: Inventing and (or) Commercializing AI
By: David B. Yoffie
This teaching notes accompanies the Numenta case, HBS No. 716-469. The focus is how to scale a new artificial intelligence technology, how to build a platform and overcome chicken-or-the-egg problems, and how to utilize open source software and licensing. View Details
- March 2019
- Case
Wattpad
By: John Deighton and Leora Kornfeld
How to run a platform to match four million writers of stories to 75 million readers? Use data science. Make money by doing deals with television and filmmakers and book publishers. The case describes the challenges of matching readers to stories and of helping writers... View Details
Keywords: Platform Businesses; Creative Industries; Publishing; Data Science; Machine Learning; Collaborative Filtering; Women And Leadership; Managing Data Scientists; Big Data; Recommender Systems; Digital Platforms; Information Technology; Intellectual Property; Analytics and Data Science; Publishing Industry; Entertainment and Recreation Industry; Canada; United States; Philippines; Viet Nam; Turkey; Indonesia; Brazil
Deighton, John, and Leora Kornfeld. "Wattpad." Harvard Business School Case 919-413, March 2019.
- March 2019
- Case
DayTwo: Going to Market with Gut Microbiome
By: Ayelet Israeli and David Lane
DayTwo is a young Israeli startup that applies research on the gut microbiome and machine learning algorithms to deliver personalized nutritional recommendations to its users in order to minimize blood sugar spikes after meals. After a first year of trial rollout in... View Details
Keywords: Start-up Growth; Startup; Positioning; Targeting; Go To Market Strategy; B2B2C; B2B Vs. B2C; Health & Wellness; AI; Machine Learning; Female Ceo; Female Protagonist; Science-based; Science And Technology Studies; Ecommerce; Applications; DTC; Direct To Consumer Marketing; US Health Care; "USA,"; Innovation; Pricing; Business Growth; Segmentation; Distribution Channels; Growth and Development Strategy; Business Startups; Science-Based Business; Health; Innovation and Invention; Marketing; Information Technology; Business Growth and Maturation; E-commerce; Applications and Software; Health Industry; Technology Industry; Insurance Industry; Information Technology Industry; Food and Beverage Industry; Israel; United States
Israeli, Ayelet, and David Lane. "DayTwo: Going to Market with Gut Microbiome." Harvard Business School Case 519-010, March 2019.
- February 2019
- Case
Miroglio Fashion (A)
By: Sunil Gupta and David Lane
Francesco Cavarero, chief information officer of Miroglio Fashion, Italy’s third-largest retailer of women’s apparel, was trying to bring analytical rigor to the company’s forecasting and inventory management decisions. But fashion is inherently hard to predict. Can... View Details
Keywords: Inventory Management; Demand Forecasting; Artificial Intelligence; Machine Learning; Forecasting and Prediction; Operations; Management; Decision Making; AI and Machine Learning; Apparel and Accessories Industry; Fashion Industry
Gupta, Sunil, and David Lane. "Miroglio Fashion (A)." Harvard Business School Case 519-053, February 2019.
- 2020
- Working Paper
Overcoming the Cold Start Problem of CRM Using a Probabilistic Machine Learning Approach
By: Eva Ascarza
The success of Customer Relationship Management (CRM) programs ultimately depends on the firm's ability to understand consumers' preferences and precisely capture how these preferences may differ across customers. Only by understanding customer heterogeneity, firms can... View Details
Keywords: Customer Management; Targeting; Deep Exponential Families; Probabilistic Machine Learning; Cold Start Problem; Customer Relationship Management; Customer Value and Value Chain; Consumer Behavior; Analytics and Data Science; Mathematical Methods; Retail Industry
Padilla, Nicolas, and Eva Ascarza. "Overcoming the Cold Start Problem of CRM Using a Probabilistic Machine Learning Approach." Harvard Business School Working Paper, No. 19-091, February 2019. (Revised May 2020. Accepted at the Journal of Marketing Research.)
- Article
Multivariate Unsupervised Machine Learning for Anomaly Detection in Enterprise Applications
By: Daniel Elsner, Pouya Aleatrati Khosroshahi, Alan MacCormack and Robert Lagerström
Existing application performance management (APM) solutions lack robust anomaly detection capabilities and root cause analysis techniques that do not require manual efforts and domain knowledge. In this paper, we develop a density-based unsupervised machine learning... View Details
Keywords: Big Data; Data Science And Analytics Management; Governance And Compliance; Organizational Systems And Technology; Anomaly Detection; Application Performance Management; Machine Learning; Enterprise Architecture; Analytics and Data Science
Elsner, Daniel, Pouya Aleatrati Khosroshahi, Alan MacCormack, and Robert Lagerström. "Multivariate Unsupervised Machine Learning for Anomaly Detection in Enterprise Applications." Proceedings of the Hawaii International Conference on System Sciences 52nd (2019): 5827–5836.
- 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.)
- 2019
- Article
An Empirical Study of Rich Subgroup Fairness for Machine Learning
By: Michael J Kearns, Seth Neel, Aaron Leon Roth and Zhiwei Steven Wu
Kearns et al. [2018] recently proposed a notion of rich subgroup fairness intended to bridge the gap between statistical and individual notions of fairness. Rich subgroup fairness picks a statistical fairness constraint (say, equalizing false positive rates across... View Details
Kearns, Michael J., Seth Neel, Aaron Leon Roth, and Zhiwei Steven Wu. "An Empirical Study of Rich Subgroup Fairness for Machine Learning." Proceedings of the Conference on Fairness, Accountability, and Transparency (2019): 100–109.
- 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).
- December 2018 (Revised March 2021)
- Background Note
Modern Automation (A): Artificial Intelligence
By: William R. Kerr and James Palano
This primer is meant to be a field guide to the late 2010s' surge in business use of "Artificial Intelligence" (AI), or enterprise software based in machine learning. First, it provides an overview of the key trends—digitization, connectivity, the continuation of... View Details
Keywords: Artificial Intelligence; Digitization; Connectivity; Computing; Future Of Work; Automation; AI and Machine Learning
Kerr, William R., and James Palano. "Modern Automation (A): Artificial Intelligence." Harvard Business School Background Note 819-084, December 2018. (Revised March 2021.)
- December 2018
- Teaching Note
Autonomous Vehicles: The Rubber Hits the Road…but When?
By: William Kerr and James Palano
The autonomous vehicles have enormous implications for business and society. But, despite the headline-laden attention paid to the technology, there remain more questions than answers. Students will learn about the complex industry and have explicit discussions about... View Details
- December 2018
- Case
Choosy
By: Jeffrey J. Bussgang and Julia Kelley
Founded in 2017, Choosy is a data-driven fashion startup that uses algorithms to identify styles trending on social media. After manufacturing similar items using a China-based supply chain, Choosy sells them to consumers through its website and social media pages.... View Details
Keywords: Artificial Intelligence; Algorithms; Machine Learning; Neural Networks; Instagram; Influencer; Fast Fashion; Design; Customer Satisfaction; Customer Focus and Relationships; Decision Making; Cost vs Benefits; Innovation and Invention; Brands and Branding; Product Positioning; Demand and Consumers; Supply Chain; Production; Logistics; Business Model; Expansion; Internet and the Web; Mobile and Wireless Technology; Digital Platforms; Social Media; Technology Industry; Fashion Industry; North and Central America; United States; New York (state, US); New York (city, NY)