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- May 2021
- Teaching Note
THE YES: Reimagining the Future of E-Commerce with Artificial Intelligence (AI)
By: Ayelet Israeli and Jill Avery
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;
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
- 2021
- Article
Fair Influence Maximization: A Welfare Optimization Approach
By: Aida Rahmattalabi, Shahin Jabbari, Himabindu Lakkaraju, Phebe Vayanos, Max Izenberg, Ryan Brown, Eric Rice and Milind Tambe
Several behavioral, social, and public health interventions, such as suicide/HIV prevention or community preparedness against natural disasters, leverage social network information to maximize outreach. Algorithmic influence maximization techniques have been proposed...
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- 2021
- Article
Does Fair Ranking Improve Minority Outcomes? Understanding the Interplay of Human and Algorithmic Biases in Online Hiring
By: Tom Sühr, Sophie Hilgard and Himabindu Lakkaraju
Ranking algorithms are being widely employed in various online hiring platforms including LinkedIn, TaskRabbit, and Fiverr. Prior research has demonstrated that ranking algorithms employed by these platforms are prone to a variety of undesirable biases, leading to the...
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- 2021
- Working Paper
Diversity and Performance in Entrepreneurial Teams
By: Sophie Calder-Wang, Paul A. Gompers and Kevin Huang
We study the role of diversity and performance in the entrepreneurial teams. We exploit a unique dataset of MBA students who participated in a required course to propose and start a real micro-business that allows us to examine horizontal diversity (i.e., within the...
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- March 2021
- Supplement
Artea (A), (B), (C), and (D): Designing Targeting Strategies
By: Eva Ascarza and Ayelet Israeli
Power Point Supplement to Teaching Note for HBS No. 521-021,521-022,521-037,521-043. This collection of exercises aims to teach students about 1)Targeting Policies; and 2)Algorithmic bias in marketing—implications, causes, and possible solutions. Part (A) focuses on...
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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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- March 2021
- Case
VideaHealth: Building the AI Factory
By: Karim R. Lakhani and Amy Klopfenstein
Florian Hillen, co-founder and CEO of VideaHealth, a startup that used artificial intelligence (AI) to detect dental conditions on x-rays, spent the early years of his company laying the groundwork for an AI factory. A process for quickly building and iterating on new...
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Keywords:
Artificial Intelligence;
Innovation and Invention;
Disruptive Innovation;
Technological Innovation;
Technology;
Software;
Technology Adoption;
Technology Platform;
Entrepreneurship;
Technology Industry;
Medical Devices and Supplies Industry;
North and Central America;
United States;
Massachusetts;
Cambridge
- 2021
- Working Paper
Manipulated Counterfactuals: Risks of Hidden Biases in Algorithmic Recourse
By: Dylan Slack, Sophie Hilgard, Himabindu Lakkaraju and Sameer Singh
- 2021
- Working Paper
Can I Still Trust You? Understanding the Impact of Distribution Shifts on Algorithmic Recourses
By: Kaivalya Rawal, Ece Kamar and Himabindu Lakkaraju
- 2021
- Working Paper
Towards Robust and Reliable Algorithmic Recourse
By: Sohini Upadhyay, Shalmali Joshi and Himabindu Lakkaraju
- 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...
View Details
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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- 2020
- Working Paper
Algorithm-Augmented Work Performance and Domain Experience: The Countervailing Forces of Ability and Aversion
By: Ryan Allen and Prithwiraj Choudhury
How does a knowledge worker’s level of domain experience affect their algorithm-augmented work performance? We propose and test theoretical predictions that domain experience has countervailing effects on algorithm-augmented performance: on one hand, domain experience...
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- 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
Beyond Individualized Recourse: Interpretable and Interactive Summaries of Actionable Recourses
By: Kaivalya Rawal and Himabindu Lakkaraju
As predictive models are increasingly being deployed in high-stakes decision-making, there has been a lot of interest in developing algorithms which can provide recourses to affected individuals. While developing such tools is important, it is even more critical to...
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- 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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- 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
- September 2020
- Technical Note
Algorithmic Bias in Marketing
By: Ayelet Israeli and Eva Ascarza
This note focuses on algorithmic bias in marketing. First, it presents a variety of marketing examples in which algorithmic bias may occur. The examples are organized around the 4 P’s of marketing – promotion, price, place and product—characterizing the marketing...
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Keywords:
Algorithmic Data;
Race And Ethnicity;
Promotion;
＂Marketing Analytics＂;
Marketing And Society;
Big Data;
Privacy;
Data-driven Management;
Data Analysis;
Data Analytics;
E-Commerce Strategy;
E-commerce;
Discrimination;
Targeting;
Targeted Advertising;
Pricing Algorithms;
Ethical Decision Making;
Customer Heterogeneity;
Marketing;
Race;
Ethnicity;
Gender;
Diversity;
Prejudice and Bias;
Marketing Communications;
Data and Data Sets;
Analysis;
Decision Making;
Ethics;
Customer Relationship Management;
Retail Industry;
Apparel and Accessories Industry;
United States
- September 2020 (Revised April 2021)
- Exercise
Artea: Designing Targeting Strategies
By: Eva Ascarza and Ayelet Israeli
This collection of exercises aims to teach students about 1)Targeting Policies; and 2)Algorithmic bias in marketing—implications, causes, and possible solutions. Part (A) focuses on A/B testing analysis and targeting. Parts (B),(C),(D) Introduce algorithmic bias. The...
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Keywords:
Algorithmic Data;
Race And Ethnicity;
Experimentation;
Promotion;
＂Marketing Analytics＂;
Marketing And Society;
Big Data;
Privacy;
Data-driven Management;
Data Analytics;
Data Analysis;
E-commerce;
E-Commerce Strategy;
Discrimination;
Targeted Advertising;
Targeted Policies;
Targeting;
Pricing Algorithms;
A/B Testing;
Ethical Decision Making;
Customer Base Analysis;
Customer Heterogeneity;
Coupons;
Marketing;
Race;
Gender;
Diversity;
Customer Relationship Management;
Marketing Communications;
Advertising;
Decision Making;
Ethics;
Retail Industry;
Apparel and Accessories Industry;
United States