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- All HBS Web
(252)
- News (51)
- Research (147)
- Events (2)
- Multimedia (3)
- Faculty Publications (79)
- Web
Protecting Data at HBS | Information Technology
Protecting Data at HBS Data Classification Levels ("DCL") To ensure a common understanding, Harvard uses a 5-step scale for data sensitivity. The higher the number, the more... View Details
- June 2018 (Revised June 2018)
- Case
Facebook Confronts a Crisis of Trust
By: William W. George and Amram Migdal
The case, “Facebook Confronts a Crisis of Trust,” starts with the crisis Facebook founder and CEO Mark Zuckerberg is facing in March 2018 over Cambridge Analytica’s accessing data from 87 million Facebook accounts in order to influence the 2016 U.S. Presidential... View Details
Keywords: Facebook; Data Privacy; Data Manipulation; Data Science; Political Campaigns; Political Influence; Voter Mobilization; Voters' Interests; Election Outcomes; Elections; Cambridge Analytica; Mark Zuckerberg; Sheryl Sandberg; Voting; Decision Making; Demographics; Ethics; Geopolitical Units; Government and Politics; Government Legislation; National Security; Political Elections; Information Management; Leadership; Leadership Style; Crisis Management; Social Psychology; Personal Characteristics; Power and Influence; Society; Public Opinion; Technology Industry; United States; United Kingdom
George, William W., and Amram Migdal. "Facebook Confronts a Crisis of Trust." Harvard Business School Case 318-145, June 2018. (Revised June 2018.)
- 22 Feb 2024
- Research & Ideas
How to Make AI 'Forget' All the Private Data It Shouldn't Have
Europe’s tougher data privacy regulations went into effect in 2018, they have created complications for companies worldwide. Questions around data View Details
- Research Summary
Overview
I have spent my career studying novel talent management practices and their effect on collaboration and performance. My core research focuses on two interrelated organizational trends that have become salient in the 21st century: workplace transparency (who gets to... View Details
Keywords: Privacy; Transparency; Productivity; Field Experiments; Communication; Design; Human Resources; Leadership; Management; Organizational Design; Organizational Structure; Performance; Groups and Teams; Networks; Behavior; Social and Collaborative Networks; Satisfaction; North America; Europe; Asia; China; Japan; Latin America
- Web
European and UK Personal Data Collection Disclosure | HBS Online
Additional Privacy Disclosures 1. Introduction These Additional Privacy Disclosures (“Disclosures”) provide information on collection and use of personal data about individuals... View Details
- Teaching Interest
Overview
By: John A. Deighton
I teach about the ecosystem of big data, the role of data in advertising and creative industries, and customer management and personal privacy in an era of individual addressability. View Details
- Teaching Interest
Overview
Paul is primarily interested in teaching data science to management students through the case method. This includes technical topics (programming and statistics) as well as higher-level management issues (digital transformation, data governance, etc.) As a research... View Details
Keywords: A/B Testing; AI; AI Algorithms; AI Creativity; Algorithm; Algorithm Bias; Algorithmic Bias; Algorithmic Fairness; Algorithms; Analytics; Application Program Interface; Artificial Intelligence; Causality; Causal Inference; Computing; Computers; Data Analysis; Data Analytics; Data Architecture; Data As A Service; Data Centers; Data Governance; Data Labeling; Data Management; Data Manipulation; Data Mining; Data Ownership; Data Privacy; Data Protection; Data Science; Data Science And Analytics Management; Data Scientists; Data Security; Data Sharing; Data Strategy; Data Visualization; Database; Data-driven Decision-making; Data-driven Management; Data-driven Operations; Datathon; Economics Of AI; Economics Of Innovation; Economics Of Information System; Economics Of Science; Forecast; Forecast Accuracy; Forecasting; Forecasting And Prediction; Information Technology; Machine Learning; Machine Learning Models; Prediction; Prediction Error; Predictive Analytics; Predictive Models; Analysis; AI and Machine Learning; Analytics and Data Science; Applications and Software; Digital Transformation; Information Management; Digital Strategy; Technology Adoption
- Web
IT Strategy: Protecting HBS Information and Data Assets | Information Technology
IT Strategy: Protecting HBS Information and Data Assets Strategic Priority: 4. Protect HBS Information & Technology Assets By Annie Harrison on December 4, 2024 Share via Facebook Share via LinkedIn Print Share via email The Opportunity A... View Details
- September 2020 (Revised June 2023)
- 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... View Details
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 Strategy; Discrimination; Targeted Advertising; Targeted Policies; Targeting; Pricing Algorithms; A/B Testing; Ethical Decision Making; Customer Base Analysis; Customer Heterogeneity; Coupons; Algorithmic Bias; Marketing; Race; Gender; Diversity; Customer Relationship Management; Marketing Communications; Advertising; Decision Making; Ethics; E-commerce; Analytics and Data Science; Retail Industry; Apparel and Accessories Industry; United States
Ascarza, Eva, and Ayelet Israeli. "Artea: Designing Targeting Strategies." Harvard Business School Exercise 521-021, September 2020. (Revised June 2023.)
- September 2020 (Revised July 2022)
- 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... View Details
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; Discrimination; Targeting; Targeted Advertising; Pricing Algorithms; Ethical Decision Making; Customer Heterogeneity; Marketing; Race; Ethnicity; Gender; Diversity; Prejudice and Bias; Marketing Communications; Analytics and Data Science; Analysis; Decision Making; Ethics; Customer Relationship Management; E-commerce; Retail Industry; Apparel and Accessories Industry; United States
Israeli, Ayelet, and Eva Ascarza. "Algorithmic Bias in Marketing." Harvard Business School Technical Note 521-020, September 2020. (Revised July 2022.)
- February 2024
- Module Note
Data-Driven Marketing in Retail Markets
By: Ayelet Israeli
This note describes an eight-class sessions module on data-driven marketing in retail markets. The module aims to familiarize students with core concepts of data-driven marketing in retail, including exploring the opportunities and challenges, adopting best practices,... View Details
Keywords: Data; Data Analytics; Retail; Retail Analytics; Data Science; Business Analytics; ＂Marketing Analytics＂; Omnichannel; Omnichannel Retailing; Omnichannel Retail; DTC; Direct To Consumer Marketing; Ethical Decision Making; Algorithmic Bias; Privacy; A/B Testing; Descriptive Analytics; Prescriptive Analytics; Predictive Analytics; Analytics and Data Science; E-commerce; Marketing Channels; Demand and Consumers; Marketing Strategy; Retail Industry
Israeli, Ayelet. "Data-Driven Marketing in Retail Markets." Harvard Business School Module Note 524-062, February 2024.
- June 2023
- Simulation
Artea Dashboard and Targeting Policy Evaluation
By: Ayelet Israeli and Eva Ascarza
Companies deploy A/B experiments to gain valuable insights about their customers in order to answer strategic business problems. In marketing, A/B tests are often used to evaluate marketing interventions intended to generate incremental outcomes for the firm. The Artea... View Details
Keywords: Algorithm Bias; Algorithmic Data; Race And Ethnicity; Experimentation; Promotion; Marketing And Society; Big Data; Privacy; Data-driven Management; Data Analysis; Data Analytics; E-Commerce Strategy; Discrimination; Targeted Advertising; Targeted Policies; 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; E-commerce; Analytics and Data Science; Retail Industry; Apparel and Accessories Industry; United States
- September–October 2013
- Article
The Dynamic Advertising Effect of Collegiate Athletics
By: Doug J. Chung
I measure the spillover effect of intercollegiate athletics on the quantity and quality of applicants to institutions of higher education in the United States, popularly known as the "Flutie Effect." I treat athletic success as a stock of goodwill that decays over... View Details
Keywords: Choice Modeling; Entertainment Marketing; Heterogeneity; Panel Data; Structural Modeling; Rights; Analytics and Data Science; Higher Education; Ethics; Consumer Behavior; Advertising; Sports; Advertising Industry; Education Industry
Chung, Doug J. "The Dynamic Advertising Effect of Collegiate Athletics." Marketing Science 32, no. 5 (September–October 2013): 679–698. (Lead article. Featured in HBS Working Knowledge.)
- 07 Jul 2021
- News
Good News for Disgraced Companies: You Can Regain Trust
- 2023
- Working Paper
Debiasing Treatment Effect Estimation for Privacy-Protected Data: A Model Auditing and Calibration Approach
By: Ta-Wei Huang and Eva Ascarza
Data-driven targeted interventions have become a powerful tool for organizations to optimize business outcomes
by utilizing individual-level data from experiments. A key element of this process is the estimation
of Conditional Average Treatment Effects (CATE), which... View Details
Huang, Ta-Wei, and Eva Ascarza. "Debiasing Treatment Effect Estimation for Privacy-Protected Data: A Model Auditing and Calibration Approach." Harvard Business School Working Paper, No. 24-034, December 2023.
- 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... View Details
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).
- January 1995 (Revised November 1996)
- Case
Avalon Information Services, Inc.
By: Lynn S. Paine and Wilda White
The Privacy Review Committee of Avalon Information Services must decide how to deal with concerns voiced by its retail supermarket customers about the privacy of consumer data collected through Avalon's point-of-sale data collection program. One customer is proposing... View Details
Keywords: Mission and Purpose; Safety; Demand and Consumers; Rights; Analytics and Data Science; Information Technology; Ethics; Information Industry
Paine, Lynn S., and Wilda White. "Avalon Information Services, Inc." Harvard Business School Case 395-036, January 1995. (Revised November 1996.)
- 23 Oct 2013
- News
How Far Is Too Far in Selling Customer Data?
Banking on Data: Great Possibilities, Great Responsibilities
Karen Mills speaks at an FDIC webinar addressing policy and consumer impact perspectives on enabling “open banking” through APIs, national vs. state privacy laws, data ownership, and liability standards. View Details
- 19 Sep 2018
- News