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    • All HBS Web  (763)
      • Faculty Publications  (209)

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      • 2023
      • Article

      Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse

      By: Martin Pawelczyk, Teresa Datta, Johannes van-den-Heuvel, Gjergji Kasneci and Himabindu Lakkaraju
      As machine learning models are increasingly being employed to make consequential decisions in real-world settings, it becomes critical to ensure that individuals who are adversely impacted (e.g., loan denied) by the predictions of these models are provided with a means...  View Details
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      Pawelczyk, Martin, Teresa Datta, Johannes van-den-Heuvel, Gjergji Kasneci, and Himabindu Lakkaraju. "Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse." International Conference on Learning Representations (ICLR) (2023).
      • January 2023
      • Case

      Replika: Embodying AI

      By: Shikhar Ghosh, Shweta Bagai and Marilyn Morgan Westner
      Keywords: AI; AI and Machine Learning; California
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      Ghosh, Shikhar, Shweta Bagai, and Marilyn Morgan Westner. "Replika: Embodying AI." Harvard Business School Case 823-090, January 2023.
      • 2022
      • Working Paper

      Nailing Prediction: Experimental Evidence on the Value of Tools in Predictive Model Development

      By: Daniel Yue, Paul Hamilton and Iavor Bojinov
      Predictive model development is understudied despite its importance to modern businesses. Although prior discussions highlight advances in methods (along the dimensions of data, computing power, and algorithms) as the primary driver of model quality, the value of tools...  View Details
      Keywords: Analytics and Data Science
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      Yue, Daniel, Paul Hamilton, and Iavor Bojinov. "Nailing Prediction: Experimental Evidence on the Value of Tools in Predictive Model Development." Harvard Business School Working Paper, No. 23-029, December 2022.
      • 2022
      • Working Paper

      Improving Human-Algorithm Collaboration: Causes and Mitigation of Over- and Under-Adherence

      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: Cognitive Biases; Algorithm Transparency; Forecasting and Prediction; Behavior; AI and Machine Learning; Analytics and Data Science; Cognition and Thinking
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      Balakrishnan, Maya, Kris Ferreira, and Jordan Tong. "Improving Human-Algorithm Collaboration: Causes and Mitigation of Over- and Under-Adherence." Working Paper, December 2022.
      • 2022
      • Working Paper

      The Regulation of Medical AI: Policy Approaches, Data, and Innovation Incentives

      By: Ariel Dora Stern
      For those who follow health and technology news, it is difficult to go more than a few days without reading about a compelling new application of Artificial Intelligence (AI) to health care. AI has myriad applications in medicine and its adjacent industries, with...  View Details
      Keywords: AI and Machine Learning; Health Care and Treatment; Governing Rules, Regulations, and Reforms; Technological Innovation; Medical Devices and Supplies Industry
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      Stern, Ariel Dora. "The Regulation of Medical AI: Policy Approaches, Data, and Innovation Incentives." NBER Working Paper Series, No. 30639, December 2022.
      • November 2022 (Revised December 2022)
      • 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 December 2022.)
      • November 2022 (Revised January 2023)
      • Case

      Hugging Face: Serving AI on a Platform

      By: Shane Greenstein, Daniel Yue, Kerry Herman and Sarah Gulick
      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, Kerry Herman, and Sarah Gulick. "Hugging Face: Serving AI on a Platform." Harvard Business School Case 623-026, November 2022. (Revised January 2023.)
      • 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
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      Shergadwala, Murtuza, Himabindu Lakkaraju, and Krishnaram Kenthapadi. "A Human-Centric Take on Model Monitoring." AAAI Conference on Human Computation and Crowdsourcing (HCOMP) (2022).
      • November 2022
      • Article

      A Language-Based Method for Assessing Symbolic Boundary Maintenance between Social Groups

      By: Anjali M. Bhatt, Amir Goldberg and Sameer B. Srivastava
      When the social boundaries between groups are breached, the tendency for people to erect and maintain symbolic boundaries intensifies. Drawing on extant perspectives on boundary maintenance, we distinguish between two strategies that people pursue in maintaining...  View Details
      Keywords: Culture; Machine Learning; Natural Language Processing; Symbolic Boundaries; Organizations; Boundaries; Social Psychology; Interpersonal Communication; Organizational Culture
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      Bhatt, Anjali M., Amir Goldberg, and Sameer B. Srivastava. "A Language-Based Method for Assessing Symbolic Boundary Maintenance between Social Groups." Sociological Methods & Research 51, no. 4 (November 2022): 1681–1720.
      • November–December 2022
      • Article

      Can AI Really Help You Sell?

      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?" Harvard Business Review (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.
      • 2022
      • Working Paper

      When Less Is More: Using Short-term Signals to Overcome Systematic Bias in Long-run Targeting

      By: Ta-Wei Huang and Eva Ascarza
      Firms are increasingly interested in developing targeted interventions for customers with the best response. Doing so requires firms to identify differences in customer sensitivity, which they often obtain using uplift modeling (i.e., heterogeneous treatment effect...  View Details
      Keywords: Long-run Targeting; Heterogeneous Treatment Effect; Statistical Surrogacy; Customer Churn; Field Experiments; Consumer Behavior; Customer Focus and Relationships; AI and Machine Learning; Marketing
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      Huang, Ta-Wei, and Eva Ascarza. "When Less Is More: Using Short-term Signals to Overcome Systematic Bias in Long-run Targeting." Harvard Business School Working Paper, No. 23-023, 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.)
      • 2022
      • Working Paper

      Communicating Corporate Culture in Labor Markets: Evidence from Job Postings

      By: Joseph Pacelli, Tianshuo Shi and Yuan Zou
      A company’s culture represents one of the most important factors that job seekers consider. In this study, we examine how firms craft their job postings to convey their cultures and whether doing so helps attract employees. We utilize state-of-the art machine learning...  View Details
      Keywords: Corporate Culture Significance; Labor Markets; Disclosure; Organizational Culture; Recruitment; Talent and Talent Management
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      Pacelli, Joseph, Tianshuo Shi, and Yuan Zou. "Communicating Corporate Culture in Labor Markets: Evidence from Job Postings." Working Paper, October 2022.
      • 2022
      • Working Paper

      What Would It Mean for a Machine to Have a Self?

      By: Julian De Freitas, Ahmet Kaan Uğuralp, Zeliha Uğuralp, Laurie Paul, Joshua B. Tenenbaum and Tomer Ullman
      What would it mean for autonomous AI agents to have a ‘self’? One proposal for a minimal notion of self is a representation of one’s body spatio-temporally located in the world, with a tag of that representation as the agent taking actions in the world. This turns...  View Details
      Keywords: Self; AI; Games; Reinforcement Learning; Avatar; AI and Machine Learning
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      De Freitas, Julian, Ahmet Kaan Uğuralp, Zeliha Uğuralp, Laurie Paul, Joshua B. Tenenbaum, and Tomer Ullman. "What Would It Mean for a Machine to Have a Self?" Harvard Business School Working Paper, No. 23-017, September 2022.
      • 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; Marketing; 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 2022 (Revised August 2022)
      • Case

      Icario Health: AI to Drive Health Engagement

      By: David C. Edelman
      Icario Health has built a market-leading AI engine to help health insurers drive better health behaviors for their members, enabling the insurers to improve their Medicare.  View Details
      Keywords: Marketing; Health Care and Treatment; AI and Machine Learning; Health Industry; United States
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      Edelman, David C. "Icario Health: AI to Drive Health Engagement." Harvard Business School Case 523-025, August 2022. (Revised August 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).
      • 2022
      • Working Paper

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

      By: Julian De Freitas, Ahmet Kaan Uğuralp and Zeliha Uğuralp
      Increasingly, some products do not merely automate some piece of our lives but act as autonomous agents. When these technologies are not yet perfected, what are their risks? Here we explore the case of AI companion apps. Although these apps are designed...  View Details
      Keywords: Autonomy; Artificial Intelligence; Chatbots; New Technology; Brand Crises; Ethics; Mental Health; AI and Machine Learning; Well-being; Health; Applications and Software
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      De Freitas, Julian, Ahmet Kaan Uğuralp, and Zeliha Uğuralp. "Ethical Risks of Autonomous Products: The Case of Mental Health Crises on AI Companion Applications." Harvard Business School Working Paper, No. 23-011, August 2022.
      • 2022
      • Working Paper

      Machine Learning Models for Prediction of Scope 3 Carbon Emissions

      By: George Serafeim and Gladys Vélez Caicedo
      For most organizations, the vast amount of carbon emissions occur in their supply chain and in the post-sale processing, usage, and end of life treatment of a product, collectively labelled scope 3 emissions. In this paper, we train machine learning algorithms on 15...  View Details
      Keywords: Carbon Emissions; Climate Change; Environment; Carbon Accounting; Machine Learning; Artificial Intelligence; Digital; Data Science; Environmental Sustainability; Environmental Management; Environmental Accounting
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      Serafeim, George, and Gladys Vélez Caicedo. "Machine Learning Models for Prediction of Scope 3 Carbon Emissions." Harvard Business School Working Paper, No. 22-080, June 2022.
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