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- Article
Eliminating Unintended Bias in Personalized Policies Using Bias-Eliminating Adapted Trees (BEAT)
By: Eva Ascarza and Ayelet Israeli
An inherent risk of algorithmic personalization is disproportionate targeting of individuals from certain groups (or demographic characteristics such as gender or race), even when the decision maker does not intend to discriminate based on those “protected”... View Details
Keywords:
Algorithm Bias;
Personalization;
Targeting;
Generalized Random Forests (GRF);
Discrimination;
Customization and Personalization;
Decision Making;
Fairness;
Mathematical Methods
Ascarza, Eva, and Ayelet Israeli. "Eliminating Unintended Bias in Personalized Policies Using Bias-Eliminating Adapted Trees (BEAT)." e2115126119. Proceedings of the National Academy of Sciences 119, no. 11 (March 8, 2022).
- March 2022
- Article
Learning to Rank an Assortment of Products
By: Kris Ferreira, Sunanda Parthasarathy and Shreyas Sekar
We consider the product ranking challenge that online retailers face when their customers typically behave as “window shoppers”: they form an impression of the assortment after browsing products ranked in the initial positions and then decide whether to continue...
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Keywords:
Online Learning;
Product Ranking;
Assortment Optimization;
Learning;
Internet and the Web;
Product Marketing;
Consumer Behavior;
E-commerce
Ferreira, Kris, Sunanda Parthasarathy, and Shreyas Sekar. "Learning to Rank an Assortment of Products." Management Science 68, no. 3 (March 2022): 1828–1848.
- Article
Adaptive Machine Unlearning
By: Varun Gupta, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi and Chris Waites
Data deletion algorithms aim to remove the influence of deleted data points from trained models at a cheaper computational cost than fully retraining those models. However, for sequences of deletions, most prior work in the non-convex setting gives valid guarantees...
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Gupta, Varun, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, and Chris Waites. "Adaptive Machine Unlearning." Advances in Neural Information Processing Systems (NeurIPS) 34 (2021).
- Article
Counterfactual Explanations Can Be Manipulated
By: Dylan Slack, Sophie Hilgard, Himabindu Lakkaraju and Sameer Singh
Counterfactual explanations are useful for both generating recourse and auditing fairness between groups. We seek to understand whether adversaries can manipulate counterfactual explanations in an algorithmic recourse setting: if counterfactual explanations indicate...
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Slack, Dylan, Sophie Hilgard, Himabindu Lakkaraju, and Sameer Singh. "Counterfactual Explanations Can Be Manipulated." Advances in Neural Information Processing Systems (NeurIPS) 34 (2021).
- November 2021 (Revised December 2021)
- Supplement
PittaRosso (B): Human and Machine Learning
By: Ayelet Israeli
This case supplements the "PittaRosso: Artificial Intelligence-Driven Pricing and Promotion" case, and provides major highlights on what happened at the company since the first case.
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Keywords:
Artificial Intelligence;
Pricing;
Pricing Algorithm;
Pricing Decisions;
Pricing Strategy;
Pricing Structure;
Promotion;
Promotions;
Online Marketing;
Data-driven Decision-making;
Data-driven Management;
Retail;
Retail Analytics;
Price;
Advertising Campaigns;
Analytics and Data Science;
Analysis;
Digital Marketing;
Budgets and Budgeting;
Marketing Strategy;
Marketing;
Transformation;
Decision Making;
AI and Machine Learning;
Retail Industry;
Italy
Israeli, Ayelet. "PittaRosso (B): Human and Machine Learning." Harvard Business School Supplement 522-047, November 2021. (Revised December 2021.)
- October 2021 (Revised March 2022)
- Supplement
PittaRosso: Artificial Intelligence-Driven Pricing and Promotion
By: Ayelet Israeli and Fabrizio Fantini
PittaRosso, a traditional Italian shoe retailer, is implementing an AI system to provide pricing and promotion recommendations. The system allows them to implement changes that would affect both the top of funnel and bottom of funnel activities for the company: once...
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Keywords:
Artificial Intelligence;
Pricing;
Pricing Algorithm;
Pricing Decisions;
Pricing Strategy;
Pricing Structure;
Promotion;
Promotions;
Online Marketing;
Data-driven Decision-making;
Data-driven Management;
Retail;
Retail Analytics;
Price;
Advertising Campaigns;
Analytics and Data Science;
Analysis;
Digital Marketing;
Budgets and Budgeting;
Marketing Strategy;
Marketing;
Transformation;
Decision Making;
Retail Industry;
Italy
- October 2021 (Revised June 2022)
- Case
PittaRosso: Artificial Intelligence-Driven Pricing and Promotion
By: Ayelet Israeli
PittaRosso, a traditional Italian shoe retailer, is implementing an AI system to provide pricing and promotion recommendations. The system allows them to implement changes that would affect both the top of funnel and bottom of funnel activities for the company: once...
View Details
Keywords:
Artificial Intelligence;
Pricing;
Pricing Algorithm;
Pricing Decisions;
Pricing Strategy;
Pricing Structure;
Promotion;
Promotions;
Online Marketing;
Data-driven Decision-making;
Data-driven Management;
Retail;
Retail Analytics;
AI;
Price;
Advertising Campaigns;
Analytics and Data Science;
Analysis;
Digital Marketing;
Budgets and Budgeting;
Marketing Strategy;
Marketing;
Transformation;
Decision Making;
AI and Machine Learning;
Retail Industry;
Italy
Israeli, Ayelet. "PittaRosso: Artificial Intelligence-Driven Pricing and Promotion." Harvard Business School Case 522-046, October 2021. (Revised June 2022.)
- 2021
- Working Paper
Invisible Primes: Fintech Lending with Alternative Data
By: Marco Di Maggio, Dimuthu Ratnadiwakara and Don Carmichael
We exploit anonymized administrative data provided by a major fintech platform to investigate whether using alternative data to assess borrowers’ creditworthiness results in broader credit access. Comparing actual outcomes of the fintech platform’s model to...
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Keywords:
Fintech Lending;
Alternative Data;
Machine Learning;
Algorithm Bias;
Finance;
Information Technology;
Financing and Loans;
Analytics and Data Science;
Credit
Di Maggio, Marco, Dimuthu Ratnadiwakara, and Don Carmichael. "Invisible Primes: Fintech Lending with Alternative Data." Harvard Business School Working Paper, No. 22-024, October 2021.
- 2021
- Working Paper
The Demand for Executive Skills
By: Stephen Hansen, Raffaella Sadun, Tejas Ramdas and Joseph B. Fuller
We use a unique corpus of job descriptions for C-suite positions to document skills requirements in top managerial occupations across a large sample of firms. A novel algorithm maps the text of each executive search into six separate skill clusters reflecting...
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Keywords:
C-Suite;
Jobs and Positions;
Competency and Skills;
Management Skills;
Job Search;
Job Design and Levels
Hansen, Stephen, Raffaella Sadun, Tejas Ramdas, and Joseph B. Fuller. "The Demand for Executive Skills." Harvard Business School Working Paper, No. 21-133, June 2021.
- 2021
- Working Paper
The Demand for Executive Skills
By: Stephen Hansen, Tejas Ramdas, Raffaella Sadun and Joseph B. Fuller
We use a unique corpus of job descriptions for C-suite positions to document skills requirements in top managerial occupations across a large sample of firms. A novel algorithm maps the text of each executive search into six separate skill clusters reflecting...
View Details
Hansen, Stephen, Tejas Ramdas, Raffaella Sadun, and Joseph B. Fuller. "The Demand for Executive Skills." NBER Working Paper Series, No. 28959, June 2021.
- 2021
- Article
To Thine Own Self Be True? Incentive Problems in Personalized Law
By: Jordan M. Barry, John William Hatfield and Scott Duke Kominers
Recent years have seen an explosion of scholarship on “personalized law.” Commentators foresee a world in which regulators armed with big data and machine learning techniques determine the optimal legal rule for every regulated party, then instantaneously disseminate...
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Keywords:
Personalized Law;
Regulation;
Regulatory Avoidance;
Regulatory Arbitrage;
Law And Economics;
Law And Technology;
Law And Artificial Intelligence;
Futurism;
Moral Hazard;
Elicitation;
Signaling;
Privacy;
Law;
Governing Rules, Regulations, and Reforms;
Information Technology;
AI and Machine Learning
Barry, Jordan M., John William Hatfield, and Scott Duke Kominers. "To Thine Own Self Be True? Incentive Problems in Personalized Law." Art. 2. William & Mary Law Review 62, no. 3 (2021).
- Article
Use of Connected Digital Products in Clinical Research Following the COVID-19 Pandemic: A Comprehensive Analysis of Clinical Trials
By: Caroline Marra, William J. Gordon and Ariel Dora Stern
Objectives: In an effort to mitigate COVID-19 related challenges for clinical research, the U.S. Food and Drug Administration (FDA) issued new guidance for the conduct of ‘virtual’ clinical trials in late March 2020. This study documents trends in the use of...
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Keywords:
Connected Digital Products;
Telehealth;
Remote Monitoring;
Health Testing and Trials;
Research;
Governing Rules, Regulations, and Reforms;
Technology
Marra, Caroline, William J. Gordon, and Ariel Dora Stern. "Use of Connected Digital Products in Clinical Research Following the COVID-19 Pandemic: A Comprehensive Analysis of Clinical Trials." BMJ Open 11, no. 6 (2021).
- May 2021 (Revised February 2022)
- 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...
View Details
Keywords:
Data;
Data Analytics;
Artificial Intelligence;
AI;
AI Algorithms;
AI Creativity;
Fashion;
Retail;
Retail Analytics;
E-Commerce Strategy;
Platform;
Platforms;
Big Data;
Preference Elicitation;
Predictive Analytics;
App Development;
"Marketing Analytics";
Advertising;
Mobile App;
Mobile Marketing;
Apparel;
Online Advertising;
Referral Rewards;
Referrals;
Female Ceo;
Female Entrepreneur;
Female Protagonist;
Analytics and Data Science;
Analysis;
Creativity;
Marketing Strategy;
Brands and Branding;
Consumer Behavior;
Demand and Consumers;
Forecasting and Prediction;
Marketing Channels;
Digital Marketing;
Internet and the Web;
Mobile and Wireless Technology;
AI and Machine Learning;
E-commerce;
Fashion Industry;
Retail Industry;
Apparel and Accessories Industry;
Consumer Products Industry;
United States
- 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...
View Details
Sühr, Tom, Sophie Hilgard, and Himabindu Lakkaraju. "Does Fair Ranking Improve Minority Outcomes? Understanding the Interplay of Human and Algorithmic Biases in Online Hiring." Proceedings of the AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society 4th (2021).
- 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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Keywords:
Economics Of AI;
Value Of Data;
Perishability;
Time Dependency;
Flow Of Data;
Data Strategy;
Analytics and Data Science;
Value;
Strategy;
Competitive Advantage
Valavi, Ehsan, Joel Hestness, Marco Iansiti, Newsha Ardalani, Feng Zhu, and Karim R. Lakhani. "Time Dependency, Data Flow, and Competitive Advantage." Harvard Business School Working Paper, No. 21-099, March 2021.
- 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;
Information Technology;
Applications and Software;
Technology Adoption;
Digital Platforms;
Entrepreneurship;
AI and Machine Learning;
Technology Industry;
Medical Devices and Supplies Industry;
North and Central America;
United States;
Massachusetts;
Cambridge
Lakhani, Karim R., and Amy Klopfenstein. "VideaHealth: Building the AI Factory." Harvard Business School Case 621-021, March 2021.
- Mar 2021
- Conference Presentation
Descent-to-Delete: Gradient-Based Methods for Machine Unlearning
By: Seth Neel, Aaron Leon Roth and Saeed Sharifi-Malvajerdi
We study the data deletion problem for convex models. By leveraging techniques from convex optimization and reservoir sampling, we give the first data deletion algorithms that are able to handle an arbitrarily long sequence of adversarial updates while promising both...
View Details
Neel, Seth, Aaron Leon Roth, and Saeed Sharifi-Malvajerdi. "Descent-to-Delete: Gradient-Based Methods for Machine Unlearning." Paper presented at the 32nd Algorithmic Learning Theory Conference, March 2021.
- 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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Keywords:
Digital Platforms;
Internet and the Web;
Knowledge Sharing;
Information Management;
Sales;
Value Creation;
Product Positioning;
Israel
Moreno, Antonio, and Danielle Golan. "Anodot: Autonomous Business Monitoring." Harvard Business School Case 621-084, January 2021.
- 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;
E-Commerce Strategy;
Platform;
Platforms;
Big Data;
Preference Elicitation;
Preference Prediction;
Predictive Analytics;
App Development;
"Marketing Analytics";
Advertising;
Mobile App;
Mobile Marketing;
Apparel;
Online Advertising;
Referral Rewards;
Referrals;
Female Ceo;
Female Entrepreneur;
Female Protagonist;
Analytics and Data Science;
Analysis;
Creativity;
Marketing Strategy;
Brands and Branding;
Consumer Behavior;
Demand and Consumers;
Forecasting and Prediction;
Marketing Channels;
Digital Marketing;
Internet and the Web;
Mobile and Wireless Technology;
AI and Machine Learning;
E-commerce;
Fashion Industry;
Retail Industry;
Apparel and Accessories Industry;
Consumer Products Industry;
United States
Avery, Jill, Ayelet Israeli, and Emma von Maur. "THE YES: Reimagining the Future of E-Commerce with Artificial Intelligence (AI)." Harvard Business School Case 521-070, January 2021. (Revised March 2021.)