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      • October 2024
      • Case

      EU's Digital Services Act and Digital Markets Act

      By: David B. Yoffie and Sarah von Bargen
      Since the early 2020s, the EU began passing regulations on digital platforms and their marketplaces. One of the first was the Digital Services Act package, consisting of the Digital Services Act (DSA) and the Digital Markets Act (DMA). These regulations were focused on... View Details
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      Yoffie, David B., and Sarah von Bargen. "EU's Digital Services Act and Digital Markets Act." Harvard Business School Case 725-372, October 2024.
      • March 2024 (Revised May 2024)
      • Case

      Amperity: First-Party Data at a Crossroads

      By: Elie Ofek, Hema Yoganarasimhan and Alexis Lefort
      In the summer of 2023, Amperity management was facing a critical decision on its future direction. Given the dramatic changes occurring within the digital advertising ecosystem, as concerns over consumer privacy placed limits on the ability to engage in third-party... View Details
      Keywords: AI and Machine Learning; Technology Adoption; Business Strategy; Digital Marketing; Price; Product; Business or Company Management; Advertising Industry
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      Ofek, Elie, Hema Yoganarasimhan, and Alexis Lefort. "Amperity: First-Party Data at a Crossroads." Harvard Business School Case 524-017, March 2024. (Revised May 2024.)
      • 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
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      Israeli, Ayelet. "Data-Driven Marketing in Retail Markets." Harvard Business School Module Note 524-062, February 2024.
      • January 2024 (Revised February 2024)
      • Course Overview Note

      Managing Customers for Growth: Course Overview for Students

      By: Eva Ascarza
      Managing Customers for Growth (MCG) is a 14-session elective course for second-year MBA students at Harvard Business School. It is designed for business professionals engaged in roles centered on customer-driven growth activities. The course explores the dynamics of... View Details
      Keywords: Customer Relationship Management; Decision Making; Analytics and Data Science; Growth Management; Telecommunications Industry; Technology Industry; Financial Services Industry; Education Industry; Travel Industry
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      Ascarza, Eva. "Managing Customers for Growth: Course Overview for Students." Harvard Business School Course Overview Note 524-032, January 2024. (Revised February 2024.)
      • January–February 2024
      • Article

      Shared Service Delivery Can Increase Client Engagement: A Study of Shared Medical Appointments

      By: Ryan W. Buell, Kamalini Ramdas, Nazlı Sönmez, Kavitha Srinivasan and Rengaraj Venkatesh
      Problem Definition: Clients and service providers alike often consider one-on-one service delivery to be ideal, assuming – perhaps unquestioningly – that devoting individualized attention best improves client outcomes. In contrast, in shared service delivery, clients... View Details
      Keywords: Health Care and Treatment; Customer Satisfaction; Outcome or Result; Performance Improvement
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      Buell, Ryan W., Kamalini Ramdas, Nazlı Sönmez, Kavitha Srinivasan, and Rengaraj Venkatesh. "Shared Service Delivery Can Increase Client Engagement: A Study of Shared Medical Appointments." Manufacturing & Service Operations Management 26, no. 1 (January–February 2024): 154–166.
      • 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
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      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.)
      • December 2023 (Revised August 2024)
      • Case

      Monsters in the Machine? Tackling the Challenge of Responsible AI

      By: Paul M. Healy and Debora L. Spar
      In November of 2022, the small tech company OpenAI released ChatGPT, an artificial intelligence chatbot which quickly captured the public’s imagination—becoming the world’s fastest-growing consumer application within months of its release. Though observers from across... View Details
      Keywords: Technological Innovation; AI and Machine Learning; Ethics; Governing Rules, Regulations, and Reforms; Technology Adoption; Corporate Social Responsibility and Impact; Technology Industry; United States; European Union; China
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      Healy, Paul M., and Debora L. Spar. "Monsters in the Machine? Tackling the Challenge of Responsible AI." Harvard Business School Case 324-062, December 2023. (Revised August 2024.)
      • 2023
      • Article

      MoPe: Model Perturbation-based Privacy Attacks on Language Models

      By: Marvin Li, Jason Wang, Jeffrey Wang and Seth Neel
      Recent work has shown that Large Language Models (LLMs) can unintentionally leak sensitive information present in their training data. In this paper, we present Model Perturbations (MoPe), a new method to identify with high confidence if a given text is in the training... View Details
      Keywords: Large Language Model; AI and Machine Learning; Cybersecurity
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      Li, Marvin, Jason Wang, Jeffrey Wang, and Seth Neel. "MoPe: Model Perturbation-based Privacy Attacks on Language Models." Proceedings of the Conference on Empirical Methods in Natural Language Processing (2023): 13647–13660.
      • November 2023 (Revised March 2024)
      • Technical Note

      Customer Data Privacy

      By: Eva Ascarza and Ta-Wei Huang
      This note provides an overview of the evolving landscape of customer data privacy in 2023. It highlights two pivotal aspects that make privacy a central concern for businesses: building and maintaining customer trust and navigating the intricate regulatory... View Details
      Keywords: Customer Relationship Management; Governance Compliance; Governing Rules, Regulations, and Reforms; Risk and Uncertainty; Reputation; Trust; Information Management; Retail Industry; Technology Industry; Financial Services Industry; Telecommunications Industry; Europe; United States
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      Ascarza, Eva, and Ta-Wei Huang. "Customer Data Privacy." Harvard Business School Technical Note 524-005, November 2023. (Revised March 2024.)
      • 2023
      • Working Paper

      Black-box Training Data Identification in GANs via Detector Networks

      By: Lukman Olagoke, Salil Vadhan and Seth Neel
      Since their inception Generative Adversarial Networks (GANs) have been popular generative models across images, audio, video, and tabular data. In this paper we study whether given access to a trained GAN, as well as fresh samples from the underlying distribution, if... View Details
      Keywords: Cybersecurity; Copyright; AI and Machine Learning; Analytics and Data Science
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      Olagoke, Lukman, Salil Vadhan, and Seth Neel. "Black-box Training Data Identification in GANs via Detector Networks." Working Paper, October 2023.
      • 2023
      • Working Paper

      In-Context Unlearning: Language Models as Few Shot Unlearners

      By: Martin Pawelczyk, Seth Neel and Himabindu Lakkaraju
      Machine unlearning, the study of efficiently removing the impact of specific training points on the trained model, has garnered increased attention of late, driven by the need to comply with privacy regulations like the Right to be Forgotten. Although unlearning is... View Details
      Keywords: AI and Machine Learning; Copyright; Information
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      Pawelczyk, Martin, Seth Neel, and Himabindu Lakkaraju. "In-Context Unlearning: Language Models as Few Shot Unlearners." Working Paper, October 2023.
      • 2023
      • Working Paper

      The Customer Journey as a Source of Information

      By: Nicolas Padilla, Eva Ascarza and Oded Netzer
      In the face of heightened data privacy concerns and diminishing third-party data access, firms are placing increased emphasis on first-party data (1PD) for marketing decisions. However, in environments with infrequent purchases, reliance on past purchases 1PD... View Details
      Keywords: Customer Journey; Privacy; Consumer Behavior; Analytics and Data Science; AI and Machine Learning; Customer Focus and Relationships
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      Padilla, Nicolas, Eva Ascarza, and Oded Netzer. "The Customer Journey as a Source of Information." Harvard Business School Working Paper, No. 24-035, October 2023. (Revised October 2023.)
      • August 2023
      • Supplement

      Apple: Privacy vs. Safety (C)

      By: Henry McGee, Nien-hê Hsieh and Kerry Herman
      In September 2021, Apple decided to delay updates to iOS and iPadOS that included features to fight child sexual abuse. While many—including prominent privacy and security experts—praised Apple, others were opposed. They saw Apple introducing features that risked... View Details
      Keywords: Corporate Social Responsibility and Impact; Values and Beliefs; Public Opinion; Applications and Software; Leadership
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      McGee, Henry, Nien-hê Hsieh, and Kerry Herman. "Apple: Privacy vs. Safety (C)." Harvard Business School Supplement 324-033, August 2023.
      • 2023
      • Article

      Evidence from the First Shared Medical Appointments (SMAs) Randomised Controlled Trial in India: SMAs Increase the Satisfaction, Knowledge, and Medication Compliance of Patients with Glaucoma

      By: Nazlı Sönmez, Kavitha Srinivasan, Rengaraj Venkatesh, Ryan W. Buell and Kamalini Ramdas
      In Shared Medical Appointments (SMAs), patients with similar conditions meet the physician together and each receives one-on-one attention. SMAs can improve outcomes and physician productivity. Yet privacy concerns have stymied adoption. In physician-deprived nations,... View Details
      Keywords: Health Care and Treatment; Customer Satisfaction; Outcome or Result; India
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      Sönmez, Nazlı, Kavitha Srinivasan, Rengaraj Venkatesh, Ryan W. Buell, and Kamalini Ramdas. "Evidence from the First Shared Medical Appointments (SMAs) Randomised Controlled Trial in India: SMAs Increase the Satisfaction, Knowledge, and Medication Compliance of Patients with Glaucoma." e0001648. PLoS Global Public Health 3, no. 7 (2023).
      • 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
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      Israeli, Ayelet, and Eva Ascarza. "Artea Dashboard and Targeting Policy Evaluation." Harvard Business School Simulation 523-707, June 2023.
      • June 2023
      • Article

      The Salary Taboo: Privacy Norms and the Diffusion of Information

      By: Zoë Cullen and Ricardo Perez-Truglia
      The limited diffusion of salary information has implications for labor markets, such as wage discrimination policies and collective bargaining. Access to salary information is believed to be limited and unequal, but there is little direct evidence on the sources of... View Details
      Keywords: Search Costs; Privacy; Norms; Compensation; Financial Industry; Field Experiment; Knowledge Dissemination; Equality and Inequality; Gender; Compensation and Benefits; Societal Protocols
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      Cullen, Zoë, and Ricardo Perez-Truglia. "The Salary Taboo: Privacy Norms and the Diffusion of Information." Art. 104890. Journal of Public Economics 222 (June 2023).
      • May 9, 2023
      • Article

      8 Questions About Using AI Responsibly, Answered

      By: Tsedal Neeley
      Generative AI tools are poised to change the way every business operates. As your own organization begins strategizing which to use, and how, operational and ethical considerations are inevitable. This article delves into eight of them, including how your organization... View Details
      Keywords: AI and Machine Learning; Organizational Change and Adaptation; Prejudice and Bias; Ethics
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      Neeley, Tsedal. "8 Questions About Using AI Responsibly, Answered." Harvard Business Review (website) (May 9, 2023).
      • May 2023
      • Article

      Equilibrium Effects of Pay Transparency

      By: Zoë B. Cullen and Bobak Pakzad-Hurson
      The public discourse around pay transparency has focused on the direct effect: how workers seek to rectify newly-disclosed pay inequities through renegotiations. The question of how wage-setting and hiring practices of the firm respond in equilibrium has received... View Details
      Keywords: Pay Transparency; Online Labor Market; Privacy; Wage Gap; Corporate Disclosure; Wages; Negotiation
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      Cullen, Zoë B., and Bobak Pakzad-Hurson. "Equilibrium Effects of Pay Transparency." Econometrica 91, no. 3 (May 2023): 765–802. (Lead Article.)
      • April 2023
      • Article

      On the Privacy Risks of Algorithmic Recourse

      By: Martin Pawelczyk, Himabindu Lakkaraju and Seth Neel
      As predictive models are increasingly being employed to make consequential decisions, there is a growing emphasis on developing techniques that can provide algorithmic recourse to affected individuals. While such recourses can be immensely beneficial to affected... View Details
      Keywords: Recourse; Privacy Threats; AI and Machine Learning; Information
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      Pawelczyk, Martin, Himabindu Lakkaraju, and Seth Neel. "On the Privacy Risks of Algorithmic Recourse." Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS) 206 (April 2023).
      • January 2023
      • Supplement

      Apple: Privacy vs. Safety (B)

      By: Henry McGee, Nien-hê Hsieh and Christian Godwin
      In 2020, as the COVID-19 pandemic swept across the globe, Apple and Google partnered to develop a contact tracing application that would collect information about users infected with the disease and notify those who they had been in contact with. While Apple/Google’s... View Details
      Keywords: Iphone; Encryption; Data Privacy; Customers; Customer Focus and Relationships; Decision Making; Ethics; Values and Beliefs; Globalized Firms and Management; Government and Politics; Health; Health Pandemics; Leadership; Markets; Safety; Social Issues; Information Technology; Telecommunications Industry; Technology Industry; Consumer Products Industry; Electronics Industry; Health Industry; United States; Europe
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      McGee, Henry, Nien-hê Hsieh, and Christian Godwin. "Apple: Privacy vs. Safety (B)." Harvard Business School Supplement 323-066, January 2023.
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