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      • 8 Sep 2023
      • Conference Presentation

      Chatbots and Mental Health: Insights into the Safety of Generative AI

      By: Julian De Freitas, K. Uguralp, Z. Uguralp and Stefano Puntoni
      Keywords: AI and Machine Learning; Well-being
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      De Freitas, Julian, K. Uguralp, Z. Uguralp, and Stefano Puntoni. "Chatbots and Mental Health: Insights into the Safety of Generative AI." Paper presented at the Business & Generative AI Workshop, Wharton School, AI at Wharton, San Francisco, CA, United States, September 8, 2023.
      • January–February 2023
      • Article

      Data-Driven COVID-19 Vaccine Development for Janssen

      By: Dimitris Bertsimas, Michael Lingzhi Li, Xinggang Liu, Jennings Xu and Najat Khan
      The COVID-19 pandemic has spurred extensive vaccine research worldwide. One crucial part of vaccine development is the phase III clinical trial that assesses the vaccine for safety and efficacy in the prevention of COVID-19. In this work, we enumerate the first... View Details
      Keywords: COVID-19; Health Testing and Trials; Forecasting and Prediction; AI and Machine Learning; Research; Pharmaceutical Industry
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      Bertsimas, Dimitris, Michael Lingzhi Li, Xinggang Liu, Jennings Xu, and Najat Khan. "Data-Driven COVID-19 Vaccine Development for Janssen." INFORMS Journal on Applied Analytics 53, no. 1 (January–February 2023): 70–84.
      • 2 Mar 2023 - 4 Mar 2023
      • Conference Presentation

      Ethical Risks of Autonomous Products: The Case of Mental Health Crises on AI Companion Applications

      By: Julian De Freitas, K. Uguralp and Z. Oguz
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      De Freitas, Julian, K. Uguralp, and Z. Oguz. "Ethical Risks of Autonomous Products: The Case of Mental Health Crises on AI Companion Applications." Paper presented at the Society for Consumer Psychology Annual Conference, San Juan, PR, March 2–4, 2023.
      • 2023
      • Article

      Experimental Evaluation of Individualized Treatment Rules

      By: Kosuke Imai and Michael Lingzhi Li
      The increasing availability of individual-level data has led to numerous applications of individualized (or personalized) treatment rules (ITRs). Policy makers often wish to empirically evaluate ITRs and compare their relative performance before implementing them in a... View Details
      Keywords: Causal Inference; Heterogeneous Treatment Effects; Precision Medicine; Uplift Modeling; Analytics and Data Science; AI and Machine Learning
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      Imai, Kosuke, and Michael Lingzhi Li. "Experimental Evaluation of Individualized Treatment Rules." Journal of the American Statistical Association 118, no. 541 (2023): 242–256.
      • 2 Mar 2023 - 4 Mar 2023
      • Conference Presentation

      Stigma Against AI Companion Applications

      By: Julian De Freitas, A. Ragnhildstveit, K. Uguralp and Z. Oguz
      Citation
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      De Freitas, Julian, A. Ragnhildstveit, K. Uguralp, and Z. Oguz. "Stigma Against AI Companion Applications." Paper presented at the Society for Consumer Psychology Annual Conference, San Juan, PR, March 2–4, 2023.
      • 2023
      • Working Paper

      When Algorithms Explain Themselves: AI Adoption and Accuracy of Experts' Decisions

      By: Himabindu Lakkaraju and Chiara Farronato
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      Lakkaraju, Himabindu, and Chiara Farronato. "When Algorithms Explain Themselves: AI Adoption and Accuracy of Experts' Decisions." Working Paper, 2023.
      • December 2022 (Revised September 2024)
      • Case

      Sword Health

      By: Regina E. Herzlinger, Annelena Lobb and Carin-Isabel Knoop
      Virgilio “V” Bento, CEO of Sword Health—a startup that provided virtual physical therapy to patients in self-insured firms via AI and sensor technology with supervision by a physical therapist with a doctorate—considered how to increase its U.S. market share. To do so,... View Details
      Keywords: Business Growth and Maturation; Competitive Strategy; Health Industry; Technology Industry
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      Herzlinger, Regina E., Annelena Lobb, and Carin-Isabel Knoop. "Sword Health." Harvard Business School Case 323-022, December 2022. (Revised September 2024.)
      • December 2022 (Revised January 2025)
      • Case

      Akooda: Charging Toward Operational Intelligence

      By: Christopher Stanton and Mel Martin
      The Akooda case describes the challenges confronting founder and CEO Yuval Gonczarowski (MBA ‘17) in 2022 as he attempts to boost sales. Launched in November 2020, Akooda was an AI company that mined 20 different sources of digital data, from tools like Slack, Google... View Details
      Keywords: Data Mining; Productivity; Monitoring; Data Analysis; AI and Machine Learning; Knowledge Management; Operations; Problems and Challenges; Employee Relationship Management; Information Technology Industry; Technology Industry; Information Industry; Boston; Israel
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      Stanton, Christopher, and Mel Martin. "Akooda: Charging Toward Operational Intelligence." Harvard Business School Case 823-018, December 2022. (Revised January 2025.)
      • 2022
      • Article

      Efficiently Training Low-Curvature Neural Networks

      By: Suraj Srinivas, Kyle Matoba, Himabindu Lakkaraju and Francois Fleuret
      Standard deep neural networks often have excess non-linearity, making them susceptible to issues such as low adversarial robustness and gradient instability. Common methods to address these downstream issues, such as adversarial training, are expensive and often... View Details
      Keywords: AI and Machine Learning
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      Srinivas, Suraj, Kyle Matoba, Himabindu Lakkaraju, and Francois Fleuret. "Efficiently Training Low-Curvature Neural Networks." Advances in Neural Information Processing Systems (NeurIPS) (2022).
      • November 2022 (Revised March 2024)
      • Case

      Replika AI: Monetizing a Chatbot

      By: Julian De Freitas and Nicole Tempest Keller
      In early 2018, Eugenia Kuyda, co-founder and CEO of San Francisco-based chatbot Replika AI, was deciding how to monetize the app she had built. Launched in 2017, Replika was a consumer AI “companion app” developed by a team of AI software engineers originally based in... View Details
      Keywords: Mental Health; Subscriber Models; TAM; Monetization Strategy; Marketing Strategy; Product Marketing; AI and Machine Learning; Applications and Software; Product Positioning; Health Disorders; Technology Industry
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      De Freitas, Julian, and Nicole Tempest Keller. "Replika AI: Monetizing a Chatbot." Harvard Business School Case 523-016, November 2022. (Revised March 2024.)
      • November 2022 (Revised December 2024)
      • Case

      Hugging Face (A): Serving AI on a Platform

      By: Shane Greenstein, Daniel Yue, Sarah Gulick and Kerry Herman
      It is fall 2022, and open-source AI model company Hugging Face is considering its three areas of priorities: platform development, supporting the open-source community, and pursuing cutting-edge scientific research. As it expands services for enterprise clients, which... View Details
      Keywords: Community; Open-source; AI and Machine Learning; Product Development; Networks; Service Delivery; Research; Governance; Business and Stakeholder Relations; Information Industry; Technology Industry; United States
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      Greenstein, Shane, Daniel Yue, Sarah Gulick, and Kerry Herman. "Hugging Face (A): Serving AI on a Platform." Harvard Business School Case 623-026, November 2022. (Revised December 2024.)
      • 2022
      • Article

      A Human-Centric Take on Model Monitoring

      By: Murtuza Shergadwala, Himabindu Lakkaraju and Krishnaram Kenthapadi
      Predictive models are increasingly used to make various consequential decisions in high-stakes domains such as healthcare, finance, and policy. It becomes critical to ensure that these models make accurate predictions, are robust to shifts in the data, do not rely on... View Details
      Keywords: AI and Machine Learning; Research and Development; Demand and Consumers
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      Shergadwala, Murtuza, Himabindu Lakkaraju, and Krishnaram Kenthapadi. "A Human-Centric Take on Model Monitoring." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing (HCOMP) 10 (2022): 173–183.
      • November–December 2022
      • Article

      Can AI Really Help You Sell?: It Can, Depending on When and How You Implement It

      By: Jim Dickie, Boris Groysberg, Benson P. Shapiro and Barry Trailer
      Many salespeople today are struggling; only 57% of them make their annual quotas, surveys show. One problem is that buying processes have evolved faster than selling processes, and buyers today can access a wide range of online resources that let them evaluate products... View Details
      Keywords: Sales; AI and Machine Learning; Customers
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      Dickie, Jim, Boris Groysberg, Benson P. Shapiro, and Barry Trailer. "Can AI Really Help You Sell? It Can, Depending on When and How You Implement It." Harvard Business Review 100, no. 6 (November–December 2022): 120–129.
      • 2022
      • Working Paper

      The Evolution of ESG Reports and the Role of Voluntary Standards

      By: Ethan Rouen, Kunal Sachdeva and Aaron Yoon
      We examine the evolution of ESG reports of S&P 500 firms from 2010 to 2021. The percentage of firms releasing these voluntary disclosures increased from 35% to 86% during this period, although the length of these documents experienced more modest growth. Using a... View Details
      Keywords: Voluntary Disclosure; Textual Analysis; Modeling And Analysis; Corporate Social Responsibility and Impact; AI and Machine Learning; Accounting
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      Rouen, Ethan, Kunal Sachdeva, and Aaron Yoon. "The Evolution of ESG Reports and the Role of Voluntary Standards." Harvard Business School Working Paper, No. 23-024, October 2022.
      • October 2022 (Revised December 2022)
      • Case

      SMART: AI and Machine Learning for Wildlife Conservation

      By: Brian Trelstad and Bonnie Yining Cao
      Spatial Monitoring and Reporting Tool (SMART), a set of software and analytical tools designed for the purpose of wildlife conservation, had demonstrated significant improvements in patrol coverage, with some observed reductions in poaching and contributing to wildlife... View Details
      Keywords: Business and Government Relations; Emerging Markets; Technology Adoption; Strategy; Management; Ethics; Social Enterprise; AI and Machine Learning; Analytics and Data Science; Natural Environment; Technology Industry; Cambodia; United States; Africa
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      Trelstad, Brian, and Bonnie Yining Cao. "SMART: AI and Machine Learning for Wildlife Conservation." Harvard Business School Case 323-036, October 2022. (Revised December 2022.)
      • 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
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      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 2022 (Revised November 2022)
      • Teaching Note

      PittaRosso: Artificial Intelligence-Driven Pricing and Promotion

      By: Ayelet Israeli
      Teaching Note for HBS Case No. 522-046. 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; Price; Advertising Campaigns; Analytics and Data Science; Analysis; Digital Marketing; Budgets and Budgeting; Marketing Strategy; Transformation; Decision Making; AI and Machine Learning; Retail Industry; Italy
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      Israeli, Ayelet. "PittaRosso: Artificial Intelligence-Driven Pricing and Promotion." Harvard Business School Teaching Note 523-020, September 2022. (Revised November 2022.)
      • August 25, 2022
      • Article

      Find the Right Pace for Your AI Rollout

      By: Rebecca Karp and Aticus Peterson
      Implementing AI can introduce disruptive change and disfranchise staff and employees. When members are reluctant to adopt a new technology, they might hesitate to use it, push back against its deployment, or use it in limited capacity — which affects the benefits an... View Details
      Keywords: AI and Machine Learning; Technology Adoption; Change Management
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      Karp, Rebecca, and Aticus Peterson. "Find the Right Pace for Your AI Rollout." Harvard Business Review Digital Articles (August 25, 2022).
      • 20 Oct 2022 - 22 Oct 2022
      • Talk

      Stigma Against AI Companion Applications

      By: Julian De Freitas, A. Ragnhildstveit and A.K. Uğuralp
      Keywords: AI and Machine Learning; Attitudes; Perception
      Citation
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      De Freitas, Julian, A. Ragnhildstveit, and A.K. Uğuralp. "Stigma Against AI Companion Applications." 53rd Association for Consumer Research Annual Conference, Denver, CO, October 20–22, 2022.
      • 2022
      • Conference Presentation

      Towards the Unification and Robustness of Post hoc Explanation Methods

      By: Sushant Agarwal, Shahin Jabbari, Chirag Agarwal, Sohini Upadhyay, Steven Wu and Himabindu Lakkaraju
      As machine learning black boxes are increasingly being deployed in critical domains such as healthcare and criminal justice, there has been a growing emphasis on developing techniques for explaining these black boxes in a post hoc manner. In this work, we analyze two... View Details
      Keywords: AI and Machine Learning
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      Agarwal, Sushant, Shahin Jabbari, Chirag Agarwal, Sohini Upadhyay, Steven Wu, and Himabindu Lakkaraju. "Towards the Unification and Robustness of Post hoc Explanation Methods." Paper presented at the 3rd Symposium on Foundations of Responsible Computing (FORC), 2022.
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