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- All HBS Web
(103)
- News (17)
- Research (67)
- Events (2)
- Multimedia (1)
- Faculty Publications (38)
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- 30 May 2024
- Research & Ideas
Racial Bias Might Be Infecting Patient Portals. Can AI Help?
messages. That’s part of the reason the authors say there may be other factors besides direct racial bias driving the results—and a key reason that they are keen to explore this data in future research. One potential factor they... View Details
- 12 Oct 2022
- Research & Ideas
When Design Enables Discrimination: Learning from Anti-Asian Bias on Airbnb
they may inadvertently exacerbate. While Airbnb has addressed bias concerns with site changes in the past, further steps could be taken to bring more anonymity to the site, Luca says. Platform design and discrimination Renting out... View Details
- 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... View Details
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.
- 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
- 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
- September 2020 (Revised February 2024)
- Teaching Note
Artea (A), (B), (C), and (D): Designing Targeting Strategies
By: Eva Ascarza and Ayelet Israeli
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 A/B testing analysis and... View Details
- 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.
- 26 Apr 2023
- In Practice
Is AI Coming for Your Job?
generate content that perpetuates existing biases. When we train these models at scale based on existing data, if the underlying data included biased information, the result is also likely to include that bias unless we intervene. One... View Details
- March 2019
- Case
Wattpad
By: John Deighton and Leora Kornfeld
How to run a platform to match four million writers of stories to 75 million readers? Use data science. Make money by doing deals with television and filmmakers and book publishers. The case describes the challenges of matching readers to stories and of helping writers... View Details
Keywords: Platform Businesses; Creative Industries; Publishing; Data Science; Machine Learning; Collaborative Filtering; Women And Leadership; Managing Data Scientists; Big Data; Recommender Systems; Digital Platforms; Information Technology; Intellectual Property; Analytics and Data Science; Publishing Industry; Entertainment and Recreation Industry; Canada; United States; Philippines; Viet Nam; Turkey; Indonesia; Brazil
Deighton, John, and Leora Kornfeld. "Wattpad." Harvard Business School Case 919-413, March 2019.
- Forthcoming
- Article
Human-Algorithm Collaboration with Private Information: Naïve Advice Weighting Behavior and Mitigation
By: Maya Balakrishnan, Kris Ferreira and Jordan Tong
Even if algorithms make better predictions than humans on average, humans may sometimes have private information which an algorithm does not have access to that can improve performance. How can we help humans effectively use and adjust recommendations made by... View Details
Keywords: AI and Machine Learning; Analytics and Data Science; Forecasting and Prediction; Digital Marketing
Balakrishnan, Maya, Kris Ferreira, and Jordan Tong. "Human-Algorithm Collaboration with Private Information: Naïve Advice Weighting Behavior and Mitigation." Management Science (forthcoming). (Pre-published online March 24, 2025.)
- 08 May 2023
- Research & Ideas
How Trump’s Anti-Immigrant Rhetoric Crushed Crowdfunding for Minority Entrepreneurs
builds upon previous research on “systemic racial bias in entrepreneurial finance,” illustrating a “more direct” connection between shifts in public attitudes and struggles experienced by minority creators in raising money for new... View Details
Keywords: by Scott Van Voorhis
- 2025
- Working Paper
Warnings and Endorsements: Improving Human-AI Collaboration in the Presence of Outliers
By: Matthew DosSantos DiSorbo, Kris Ferreira, Maya Balakrishnan and Jordan Tong
Problem definition: While artificial intelligence (AI) algorithms may perform well on data that are representative of the training set (inliers), they may err when extrapolating on non-representative data (outliers). How can humans and algorithms work together to make... View Details
DosSantos DiSorbo, Matthew, Kris Ferreira, Maya Balakrishnan, and Jordan Tong. "Warnings and Endorsements: Improving Human-AI Collaboration in the Presence of Outliers." Working Paper, May 2025.
- 08 May 2018
- First Look
First Look at New Research and Ideas, May 8, 2018
unexpected networking opportunities, generating a tight community of German businesspeople in India. Publisher's link: https://www.hbs.edu/faculty/Pages/item.aspx?num=54465 How Scheduling Can Bias Quality Assessment: Evidence from Food... View Details
Keywords: Sean Silverthorne
- September 2020 (Revised July 2022)
- Supplement
Spreadsheet Supplement to Artea (B) and (C)
By: Eva Ascarza and Ayelet Israeli
Spreadsheet Supplement to "Artea (B): Including Customer-level Demographic Data" and "Artea (C): Potential Discrimination through Algorithmic Targeting" View Details
- 2025
- Working Paper
Enhancing Treatment Effect Prediction on Privacy-Protected Data: An Honest Post-Processing Approach
By: Ta-Wei Huang and Eva Ascarza
As firms increasingly rely on customer data for personalization, concerns over privacy and regulatory compliance have grown. Local Differential Privacy (LDP) offers strong individual-level protection by injecting noise into data before collection. While... View Details
Keywords: Targeted Intervention; Conditional Average Treatment Effect Estimation; Differential Privacy; Honest Estimation; Post-processing; Analytics and Data Science; Consumer Behavior; Marketing
Huang, Ta-Wei, and Eva Ascarza. "Enhancing Treatment Effect Prediction on Privacy-Protected Data: An Honest Post-Processing Approach." Harvard Business School Working Paper, No. 24-034, December 2023. (Revised March 2025.)
- March 2017 (Revised September 2017)
- Case
Facebook Fake News in the Post-Truth World
By: John R. Wells and Carole A. Winkler
In January 2017, Mark Zuckerberg, founder and CEO of Facebook, was surrounded by controversy. The election of Donald Trump as the next president of the United States in November 2016 had triggered a national storm of protests, and many attributed Trump’s victory to... View Details
Keywords: Facebook; Fake News; Mark Zuckerberg; Donald Trump; Algorithms; Social Networks; Partisanship; Social Media; App Development; Instagram; WhatsApp; Smartphone; Silicon Valley; Office Space; Digital Strategy; Democracy; Entry Barriers; Online Platforms; Controversy; Tencent; Agility; Social Networking; Gaming; Gaming Industry; Computer Games; Mobile Gaming; Messaging; Monetization Strategy; Advertising; Digital Marketing; Business Ventures; Acquisition; Mergers and Acquisitions; Business Growth and Maturation; Business Headquarters; Business Organization; For-Profit Firms; Trends; Communication; Communication Technology; Forms of Communication; Interactive Communication; Interpersonal Communication; Talent and Talent Management; Crime and Corruption; Voting; Demographics; Entertainment; Games, Gaming, and Gambling; Moral Sensibility; Values and Beliefs; Initial Public Offering; Profit; Revenue; Geography; Geographic Location; Global Range; Local Range; Country; Cross-Cultural and Cross-Border Issues; Globalized Firms and Management; Globalized Markets and Industries; Governing Rules, Regulations, and Reforms; Government and Politics; International Relations; National Security; Political Elections; Business History; Recruitment; Selection and Staffing; Information Management; Information Publishing; News; Newspapers; Innovation and Management; Innovation Strategy; Technological Innovation; Knowledge Dissemination; Human Capital; Law; Leadership Development; Leadership Style; Leading Change; Business or Company Management; Crisis Management; Goals and Objectives; Growth and Development Strategy; Growth Management; Management Practices and Processes; Management Style; Management Systems; Management Teams; Managerial Roles; Marketing Channels; Social Marketing; Network Effects; Market Entry and Exit; Digital Platforms; Marketplace Matching; Industry Growth; Industry Structures; Monopoly; Media; Product Development; Service Delivery; Corporate Social Responsibility and Impact; Mission and Purpose; Organizational Change and Adaptation; Organizational Culture; Organizational Structure; Public Ownership; Problems and Challenges; Business and Community Relations; Business and Government Relations; Groups and Teams; Networks; Rank and Position; Opportunities; Behavior; Emotions; Identity; Power and Influence; Prejudice and Bias; Reputation; Social and Collaborative Networks; Status and Position; Trust; Society; Civil Society or Community; Culture; Public Opinion; Social Issues; Societal Protocols; Strategy; Adaptation; Business Strategy; Commercialization; Competition; Competitive Advantage; Competitive Strategy; Corporate Strategy; Customization and Personalization; Diversification; Expansion; Horizontal Integration; Segmentation; Information Technology; Internet and the Web; Mobile and Wireless Technology; Internet and the Web; Applications and Software; Information Infrastructure; Digital Platforms; Internet and the Web; Mobile and Wireless Technology; Valuation; Advertising Industry; Communications Industry; Entertainment and Recreation Industry; Information Industry; Information Technology Industry; Journalism and News Industry; Media and Broadcasting Industry; Service Industry; Technology Industry; Telecommunications Industry; Video Game Industry; United States; California; Sunnyvale; Russia
Wells, John R., and Carole A. Winkler. "Facebook Fake News in the Post-Truth World." Harvard Business School Case 717-473, March 2017. (Revised September 2017.)
- 06 Oct 2015
- First Look
October 6, 2015
themselves and the charity, they respond very similarly to self risk and charity risk. By contrast, when their decisions force tradeoffs between money for themselves and the charity, participants act more averse to charity risk and less averse to self risk. These... View Details
Keywords: Sean Silverthorne
- November, 2016
- Article
Fixing Discrimination in Online Marketplaces
By: Ray Fisman and Michael Luca
Online marketplaces such as eBay, Uber, and Airbnb have the potential to reduce racial, gender, and other forms of bias that affect the off-line world. And in the early days of Internet commerce, the relative anonymity of transactions did make it harder for... View Details
Fisman, Ray, and Michael Luca. "Fixing Discrimination in Online Marketplaces." Harvard Business Review 94, no. 12 (November, 2016): 88–95.
- October–December 2022
- Article
Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem
By: Mochen Yang, Edward McFowland III, Gordon Burtch and Gediminas Adomavicius
Combining machine learning with econometric analysis is becoming increasingly prevalent in both research and practice. A common empirical strategy involves the application of predictive modeling techniques to "mine" variables of interest from available data, followed... View Details
Keywords: Machine Learning; Econometric Analysis; Instrumental Variable; Random Forest; Causal Inference; AI and Machine Learning; Forecasting and Prediction
Yang, Mochen, Edward McFowland III, Gordon Burtch, and Gediminas Adomavicius. "Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem." INFORMS Journal on Data Science 1, no. 2 (October–December 2022): 138–155.
- September 2019 (Revised September 2019)
- Case
Facebook Fake News in the Post-Truth World
By: John R. Wells, Carole A. Winkler and Benjamin Weinstock
In August 2019, Mark Zuckerberg, founder and CEO of Facebook, was surrounded by controversy. The first major storm of protest followed the surprise election of Donald Trump as President of the United States on November 8, 2016; many put the blame at the door of fake... View Details
Keywords: Facebook; Fake News; Mark Zuckerberg; Donald Trump; Algorithms; Social Networks; Partisanship; Social Media; App Development; Instagram; WhatsApp; Smartphone; Silicon Valley; Office Space; Digital Strategy; Democracy; Entry Barriers; Online Platforms; Controversy; Tencent; Agility; Social Networking; Gaming; Gaming Industry; Computer Games; Mobile Gaming; Messaging; Monetization Strategy; Advertising; Digital Marketing; Business Ventures; Acquisition; Mergers and Acquisitions; Business Growth and Maturation; Business Headquarters; Business Organization; For-Profit Firms; Trends; Communication; Communication Technology; Forms of Communication; Interactive Communication; Interpersonal Communication; Talent and Talent Management; Crime and Corruption; Voting; Demographics; Entertainment; Games, Gaming, and Gambling; Moral Sensibility; Values and Beliefs; Initial Public Offering; Profit; Revenue; Geography; Geographic Location; Global Range; Local Range; Country; Cross-Cultural and Cross-Border Issues; Globalized Firms and Management; Globalized Markets and Industries; Governing Rules, Regulations, and Reforms; Government and Politics; International Relations; National Security; Political Elections; Business History; Recruitment; Selection and Staffing; Information Management; Information Publishing; News; Newspapers; Innovation and Management; Innovation Strategy; Technological Innovation; Knowledge Dissemination; Human Capital; Law; Leadership Development; Leadership Style; Leading Change; Business or Company Management; Crisis Management; Goals and Objectives; Growth and Development Strategy; Growth Management; Management Practices and Processes; Management Style; Management Systems; Management Teams; Managerial Roles; Marketing Channels; Social Marketing; Network Effects; Market Entry and Exit; Digital Platforms; Marketplace Matching; Industry Growth; Industry Structures; Monopoly; Media; Product Development; Service Delivery; Corporate Social Responsibility and Impact; Mission and Purpose; Organizational Change and Adaptation; Organizational Culture; Organizational Structure; Public Ownership; Problems and Challenges; Business and Community Relations; Business and Government Relations; Groups and Teams; Networks; Rank and Position; Opportunities; Behavior; Emotions; Identity; Power and Influence; Prejudice and Bias; Reputation; Social and Collaborative Networks; Status and Position; Trust; Society; Civil Society or Community; Culture; Public Opinion; Social Issues; Societal Protocols; Strategy; Adaptation; Business Strategy; Commercialization; Competition; Competitive Advantage; Competitive Strategy; Corporate Strategy; Customization and Personalization; Diversification; Expansion; Horizontal Integration; Segmentation; Information Technology; Internet and the Web; Mobile and Wireless Technology; Applications and Software; Information Infrastructure; Valuation; Advertising Industry; Communications Industry; Entertainment and Recreation Industry; Information Industry; Information Technology Industry; Journalism and News Industry; Media and Broadcasting Industry; Service Industry; Technology Industry; Telecommunications Industry; Video Game Industry; United States; California; Sunnyvale; Russia
Wells, John R., Carole A. Winkler, and Benjamin Weinstock. "Facebook Fake News in the Post-Truth World." Harvard Business School Case 720-373, September 2019. (Revised September 2019.)