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- 2020
- Working Paper
Is Accounting Useful for Forecasting GDP Growth? A Machine Learning Perspective
By: Srikant Datar, Apurv Jain, Charles C.Y. Wang and Siyu Zhang
We provide a comprehensive examination of whether, to what extent, and which accounting variables are useful for improving the predictive accuracy of GDP growth forecasts. We leverage statistical models that accommodate a broad set of (341) variables—outnumbering the...
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- April 2021
- Article
Work-From-Anywhere: The Productivity Effects of Geographical Flexibility
By: Prithwiraj Choudhury, Cirrus Foroughi and Barbara Larson
An emerging form of remote work allows employees to work-from-anywhere, so that the worker can choose to live in a preferred geographic location. While traditional work-from-home (WFH) programs offer the worker temporal flexibility, work-from-anywhere (WFA) programs...
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- 2021
- Working Paper
Time Dependency, Data Flow, and Competitive Advantage
Data is fundamental to machine learning-based products and services and is considered strategic due to its externalities for businesses, governments, non-profits, and more generally for society. It is renowned that the value of organizations (businesses, government...
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- February 2021
- Tutorial
Assessing Prediction Accuracy of Machine Learning Models
By: Michael Toffel and Natalie Epstein
This video describes how to assess the accuracy of machine learning prediction models, primarily in the context of machine learning models that predict binary outcomes, such as logistic regression, random forest, or nearest neighbor models. After introducing and...
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- February 2021
- Case
Digital Manufacturing at Amgen
By: Shane Greenstein, Kyle Myers and Sarah Mehta
This case discusses efforts made by biotechnology (biotech) company Amgen to introduce digital technologies into its manufacturing processes. Doing so is complicated by the fact that the process for manufacturing biologics—or therapeutics made from living cells—is...
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Keywords:
Digital Technologies;
Change;
Change Management;
Decision Making;
Cost vs Benefits;
Decisions;
Information;
Data and Data Sets;
Innovation and Invention;
Innovation and Management;
Innovation Leadership;
Innovation Strategy;
Technological Innovation;
Jobs and Positions;
Knowledge;
Leadership;
Organizational Culture;
Science;
Strategy;
Technology;
Technology Adoption;
Biotechnology Industry;
Pharmaceutical Industry;
United States;
California;
Puerto Rico;
Rhode Island
- January 2021
- Case
Anodot: Autonomous Business Monitoring
By: Antonio Moreno and Danielle Golan
Autonomous business monitoring platform Anodot leveraged machine learning to provide real-time alerts regarding business anomalies. Anodot’s solution was used in various industries in order to primarily monitor business health, such as revenue and payments, product...
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- January 2021 (Revised March 2021)
- Case
THE YES: Reimagining the Future of E-Commerce with Artificial Intelligence (AI)
By: Jill Avery, Ayelet Israeli and Emma von Maur
THE YES, a multi-brand shopping app launched in May 2020 offered a new type of buying experience for women’s fashion, driven by a sophisticated algorithm that used data science and machine learning to create and deliver a personalized store for every shopper, based on...
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Keywords:
Data;
Data Analytics;
Artificial Intelligence;
Ai;
Ai Algorithms;
Ai Creativity;
Fashion;
Retail;
Retail Analytics;
Digital Marketing;
E-commerce;
E-commerce Strategy;
Platform;
Platforms;
Big Data;
Preference Elicitation;
Preference Prediction;
Predictive Analytics;
App Development;
＂marketing Analytics＂;
Advertising;
Mobile App;
Mobile Marketing;
Apparel;
Referral Rewards;
Referrals;
Female Ceo;
Female Entrepreneur;
Female Protagonist;
Data and Data Sets;
Analysis;
Creativity;
Marketing Strategy;
Brands and Branding;
Consumer Behavior;
Demand and Consumers;
Forecasting and Prediction;
Marketing Channels;
Online Advertising;
Online Technology;
Mobile Technology;
Fashion Industry;
Retail Industry;
Apparel and Accessories Industry;
Consumer Products Industry;
United States
- January 2021
- Article
Machine Learning for Pattern Discovery in Management Research
By: Prithwiraj Choudhury, Ryan Allen and Michael G. Endres
Supervised machine learning (ML) methods are a powerful toolkit for discovering robust patterns in quantitative data. The patterns identified by ML could be used for exploratory inductive or abductive research, or for post-hoc analysis of regression results to detect...
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- 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;
Technology;
Information Technology;
Software;
Health Care and Treatment;
Product Development;
Health Industry;
Information Technology Industry;
Technology Industry;
United States;
Houston;
Texas
- 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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- 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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- 2020
- Conference Presentation
An Empirical Study of the Trade-Offs Between Interpretability and Fairness
By: Shahin Jabbari, Han-Ching Ou, Himabindu Lakkaraju and Milind Tambe
- 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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- 2020
- Working Paper
Translating Information into Action: A Public Health Experiment in Bangladesh
By: Reshmaan Hussam, Kailash Pandey, Abu Shonchoy and Chikako Yamauchi
Standard models of technology adoption posit learning as the basis of adoption. However, hygiene campaigns centered around information provision have been overwhelmingly unsuccessful in changing behavior and improving child health across the developing world. We design...
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- 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;
Technology;
Business Growth and Maturation;
Health Industry;
Technology Industry;
Insurance Industry;
Information Technology Industry;
Food and Beverage Industry;
Israel;
United States
- 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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- Article
Conversational Receptiveness: Expressing Engagement with Opposing Views
By: M. Yeomans, J. Minson, H. Collins, H. Chen and F. Gino
We examine “conversational receptiveness”—the use of language to communicate one’s willingness to thoughtfully engage with opposing views. We develop an interpretable machine-learning algorithm to identify the linguistic profile of receptiveness (Studies 1A-B). We then...
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- 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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- 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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- 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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