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All HBS Web
(1,366)
- Faculty Publications (465)
- 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...
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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.)
- 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.
- 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
Incorporating Interpretable Output Constraints in Bayesian Neural Networks
By: Wanqian Yang, Lars Lorch, Moritz Graule, Himabindu Lakkaraju and Finale Doshi-Velez
Domains where supervised models are deployed often come with task-specific constraints, such as prior expert knowledge on the ground-truth function, or desiderata like safety and fairness. We introduce a novel probabilistic framework for reasoning with such constraints...
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Yang, Wanqian, Lars Lorch, Moritz Graule, Himabindu Lakkaraju, and Finale Doshi-Velez. "Incorporating Interpretable Output Constraints in Bayesian Neural Networks." Advances in Neural Information Processing Systems (NeurIPS) 33 (2020).
- Article
Robust and Stable Black Box Explanations
By: Himabindu Lakkaraju, Nino Arsov and Osbert Bastani
As machine learning black boxes are increasingly being deployed in real-world applications, there
has been a growing interest in developing post hoc explanations that summarize the behaviors
of these black boxes. However, existing algorithms for generating such...
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Lakkaraju, Himabindu, Nino Arsov, and Osbert Bastani. "Robust and Stable Black Box Explanations." Proceedings of the International Conference on Machine Learning (ICML) 37th (2020): 5628–5638. (Published in PMLR, Vol. 119.)
- Article
Soul and Machine (Learning)
By: Davide Proserpio, John R. Hauser, Xiao Liu, Tomomichi Amano, Burnap Alex, Tong Guo, Dokyun (DK) Lee, Randall Lewis, Kanishka Misra, Eric Schwarz, Artem Timoshenko, Lilei Xu and Hema Yoganarasimhan
Machine learning is bringing us self-driving cars, medical diagnoses, and language translation, but how can machine learning help marketers improve marketing decisions? Machine learning models predict extremely well, are scalable to “big data,” and are a natural fit to...
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Keywords:
Machine Learning;
Marketing Applications;
Knowledge;
Technological Innovation;
Core Relationships;
Marketing;
Applications and Software
Proserpio, Davide, John R. Hauser, Xiao Liu, Tomomichi Amano, Burnap Alex, Tong Guo, Dokyun (DK) Lee, Randall Lewis, Kanishka Misra, Eric Schwarz, Artem Timoshenko, Lilei Xu, and Hema Yoganarasimhan. "Soul and Machine (Learning)." Marketing Letters 31, no. 4 (December 2020): 393–404.
- November 2020
- Teaching Note
DayTwo: Going to Market with Gut Microbiome
By: Ayelet Israeli
Teaching Note for HBS Case No. 519-010. 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....
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Keywords:
Start-up Growth;
Startup;
Positioning;
Targeting;
Go To Market Strategy;
B2B Vs. B2C;
B2B2C;
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;
Food and Beverage Industry;
Food and Beverage Industry;
Food and Beverage Industry;
Food and Beverage Industry;
Food and Beverage Industry;
Israel;
United States
- September 2020
- Case
True North: Pioneering Analytics, Algorithms and Artificial Intelligence
By: Karim R. Lakhani, Kairavi Dey and Hannah Mayer
True North was a private equity fund that specialized in the growth and buyout of mid-market, India-centric companies. The leadership team initially believed that technology was not core to traditional businesses and steered clear of new age technology-oriented...
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Keywords:
Artificial Intelligence;
Information Technology;
Management;
Operations;
Organizations;
Leadership;
Innovation and Invention;
Business Model;
AI and Machine Learning;
Computer Industry;
Technology Industry
Lakhani, Karim R., Kairavi Dey, and Hannah Mayer. "True North: Pioneering Analytics, Algorithms and Artificial Intelligence." Harvard Business School Case 621-042, September 2020.
- 2020
- Working Paper
Design Rules, Volume 2: How Technology Shapes Organizations: Chapter 7 The Value Structure of Technologies, Part 2: Strategy without Numbers
Functional analysis as set forth in the last chapter decomposes a technical system into functional components that do things to advance the system’s purpose and the goals of its designers. Functional analysis in turn can be used to construct value structure maps...
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Keywords:
Modularity;
Value Structure Mapping;
Value Capture;
Information Technology;
Organizations;
Strategy;
Value Creation
Baldwin, Carliss Y. "Design Rules, Volume 2: How Technology Shapes Organizations: Chapter 7 The Value Structure of Technologies, Part 2: Strategy without Numbers." Harvard Business School Working Paper, No. 21-040, September 2020.
- 2020
- Working Paper
(When) Does Appearance Matter? Evidence from a Randomized Controlled Trial
By: Prithwiraj Choudhury, Tarun Khanna, Christos A. Makridis and Subhradip Sarker
While there is evidence about labor market discrimination based on race, religion, and gender, we know little about whether physical appearance leads to discrimination in labor market outcomes. We deploy a randomized experiment on 1,000 respondents in India between...
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Keywords:
Behavioral Economics;
Coronavirus;
Discrimination;
Homophily;
Labor Market Mobility;
Limited Attention;
Resumes;
Personal Characteristics;
Prejudice and Bias
Choudhury, Prithwiraj, Tarun Khanna, Christos A. Makridis, and Subhradip Sarker. "(When) Does Appearance Matter? Evidence from a Randomized Controlled Trial." Harvard Business School Working Paper, No. 21-038, September 2020.
- 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.)
- September 2020
- Article
Creativity, Artificial Intelligence, and a World of Surprises
In recent years, progress has been made toward AI Creativity, which I define as the production of highly novel, yet appropriate, ideas, problem solutions, or other outputs by autonomous machines. I argue that organizational researchers of creativity and innovation...
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Keywords:
Artificial Intelligence;
AI Creativity;
Computer Science;
Organizational Behavior;
Psychology;
Creativity;
Technological Innovation;
AI and Machine Learning
Amabile, Teresa M. "Creativity, Artificial Intelligence, and a World of Surprises." Academy of Management Discoveries 6, no. 3 (September 2020): 351–354.
- August 2020 (Revised September 2020)
- Technical Note
Assessing Prediction Accuracy of Machine Learning Models
The note introduces a variety of methods to assess the accuracy of machine learning prediction models. The note begins by briefly introducing machine learning, overfitting, training versus test datasets, and cross validation. The following accuracy metrics and tools...
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Keywords:
Machine Learning;
Statistics;
Econometric Analyses;
Experimental Methods;
Data Analysis;
Data Analytics;
Forecasting and Prediction;
Analytics and Data Science;
Analysis;
Mathematical Methods
Toffel, Michael W., Natalie Epstein, Kris Ferreira, and Yael Grushka-Cockayne. "Assessing Prediction Accuracy of Machine Learning Models." Harvard Business School Technical Note 621-045, August 2020. (Revised September 2020.)
- 2021
- Working Paper
Time and the Value of Data
By: Ehsan Valavi, Joel Hestness, Newsha Ardalani and Marco Iansiti
Managers often believe that collecting more data will continually improve the accuracy of their machine learning models. However, we argue in this paper that when data lose relevance over time, it may be optimal to collect a limited amount of recent data instead of... View Details
Keywords:
Economics Of AI;
Machine Learning;
Non-stationarity;
Perishability;
Value Depreciation;
Analytics and Data Science;
Value
Valavi, Ehsan, Joel Hestness, Newsha Ardalani, and Marco Iansiti. "Time and the Value of Data." Harvard Business School Working Paper, No. 21-016, August 2020. (Revised November 2021.)
- August 2020 (Revised February 2021)
- Case
Luckin Coffee (A): Caffeine-fueled Growth?
By: Ramon Casadesus-Masanell and Karen Elterman
This case describes the founding of Chinese coffee chain Luckin Coffee in 2017 and its path to surpassing Starbucks as the largest coffee chain in China (by number of stores) in 2019. Unlike Starbucks stores, which were designed to be welcoming “third places” for...
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Keywords:
Business Model;
Business Earnings;
Cost;
Cost Management;
Financial Statements;
Financial Condition;
Financial Management;
Stocks;
Profit;
Revenue;
Price;
Food;
Business History;
Employment;
Brands and Branding;
Product Positioning;
Marketing Strategy;
Business Strategy;
Expansion;
Competitive Strategy;
Food and Beverage Industry;
Food and Beverage Industry;
Asia;
China
Casadesus-Masanell, Ramon, and Karen Elterman. "Luckin Coffee (A): Caffeine-fueled Growth?" Harvard Business School Case 721-370, August 2020. (Revised February 2021.)
- August 2020
- Article
Machine Learning and Human Capital Complementarities: Experimental Evidence on Bias Mitigation
By: Prithwiraj Choudhury, Evan Starr and Rajshree Agarwal
The use of machine learning (ML) for productivity in the knowledge economy requires considerations of important biases that may arise from ML predictions. We define a new source of bias related to incompleteness in real time inputs, which may result from strategic...
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Choudhury, Prithwiraj, Evan Starr, and Rajshree Agarwal. "Machine Learning and Human Capital Complementarities: Experimental Evidence on Bias Mitigation." Strategic Management Journal 41, no. 8 (August 2020): 1381–1411.
- 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.
- July 2020 (Revised September 2020)
- Case
MobSquad
By: Prithwiraj Choudhury, William R. Kerr and Susie L. Ma
Irfhan Rawji (MBA 2004) launched MobSquad in October 2018 to help American tech start-ups retain hard-to-find talent, many of whom struggled with U.S. work visa issues, such as software engineers with experience in artificial intelligence, machine learning, or data...
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Keywords:
Work Visas;
H1-B;
Business Ventures;
Business Startups;
Labor;
Human Capital;
Human Resources;
Crisis Management;
Employment Industry;
Canada;
United States
Choudhury, Prithwiraj, William R. Kerr, and Susie L. Ma. "MobSquad." Harvard Business School Case 821-010, July 2020. (Revised September 2020.)
- Article
Oracle Efficient Private Non-Convex Optimization
By: Seth Neel, Aaron Leon Roth, Giuseppe Vietri and Zhiwei Steven Wu
One of the most effective algorithms for differentially private learning and optimization is objective perturbation. This technique augments a given optimization problem (e.g. deriving from an ERM problem) with a random linear term, and then exactly solves it....
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Neel, Seth, Aaron Leon Roth, Giuseppe Vietri, and Zhiwei Steven Wu. "Oracle Efficient Private Non-Convex Optimization." Proceedings of the International Conference on Machine Learning (ICML) 37th (2020).
- 2021
- Conference Presentation
An Algorithmic Framework for Fairness Elicitation
By: Christopher Jung, Michael J. Kearns, Seth Neel, Aaron Leon Roth, Logan Stapleton and Zhiwei Steven Wu
We consider settings in which the right notion of fairness is not captured by simple mathematical definitions (such as equality of error rates across groups), but might be more complex and nuanced and thus require elicitation from individual or collective stakeholders....
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Jung, Christopher, Michael J. Kearns, Seth Neel, Aaron Leon Roth, Logan Stapleton, and Zhiwei Steven Wu. "An Algorithmic Framework for Fairness Elicitation." Paper presented at the 2nd Symposium on Foundations of Responsible Computing (FORC), 2021.