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    • All HBS Web  (373)
      • Faculty Publications  (110)

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      • October 2023
      • Article

      Matching Mechanisms for Refugee Resettlement

      By: David Delacrétaz, Scott Duke Kominers and Alexander Teytelboym
      Current refugee resettlement processes account for neither the preferences of refugees nor the priorities of hosting communities. We introduce a new framework for matching with multidimensional knapsack constraints that captures the (possibly multidimensional) sizes of...  View Details
      Keywords: Refugee Resettlement; Matching; Matching Markets; Matching Platform; Matching With Contracts; Algorithms; Refugees; Market Design
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      Delacrétaz, David, Scott Duke Kominers, and Alexander Teytelboym. "Matching Mechanisms for Refugee Resettlement." American Economic Review 113, no. 10 (October 2023): 2689–2717.
      • September–October 2023
      • Article

      Interpretable Matrix Completion: A Discrete Optimization Approach

      By: Dimitris Bertsimas and Michael Lingzhi Li
      We consider the problem of matrix completion on an n × m matrix. We introduce the problem of interpretable matrix completion that aims to provide meaningful insights for the low-rank matrix using side information. We show that the problem can be...  View Details
      Keywords: Mathematical Methods
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      Bertsimas, Dimitris, and Michael Lingzhi Li. "Interpretable Matrix Completion: A Discrete Optimization Approach." INFORMS Journal on Computing 35, no. 5 (September–October 2023): 952–965.
      • 2023
      • Article

      On Minimizing the Impact of Dataset Shifts on Actionable Explanations

      By: Anna P. Meyer, Dan Ley, Suraj Srinivas and Himabindu Lakkaraju
      The Right to Explanation is an important regulatory principle that allows individuals to request actionable explanations for algorithmic decisions. However, several technical challenges arise when providing such actionable explanations in practice. For instance, models...  View Details
      Keywords: Mathematical Methods; Analytics and Data Science
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      Meyer, Anna P., Dan Ley, Suraj Srinivas, and Himabindu Lakkaraju. "On Minimizing the Impact of Dataset Shifts on Actionable Explanations." Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI) 39th (2023): 1434–1444.
      • 2023
      • Article

      On the Impact of Actionable Explanations on Social Segregation

      By: Ruijiang Gao and Himabindu Lakkaraju
      As predictive models seep into several real-world applications, it has become critical to ensure that individuals who are negatively impacted by the outcomes of these models are provided with a means for recourse. To this end, there has been a growing body of research...  View Details
      Keywords: Forecasting and Prediction; AI and Machine Learning; Outcome or Result
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      Gao, Ruijiang, and Himabindu Lakkaraju. "On the Impact of Actionable Explanations on Social Segregation." Proceedings of the International Conference on Machine Learning (ICML) 40th (2023): 10727–10743.
      • 2023
      • Article

      Towards Bridging the Gaps between the Right to Explanation and the Right to Be Forgotten

      By: Himabindu Lakkaraju, Satyapriya Krishna and Jiaqi Ma
      The Right to Explanation and the Right to be Forgotten are two important principles outlined to regulate algorithmic decision making and data usage in real-world applications. While the right to explanation allows individuals to request an actionable explanation for an...  View Details
      Keywords: Analytics and Data Science; AI and Machine Learning; Decision Making; Governing Rules, Regulations, and Reforms
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      Lakkaraju, Himabindu, Satyapriya Krishna, and Jiaqi Ma. "Towards Bridging the Gaps between the Right to Explanation and the Right to Be Forgotten." Proceedings of the International Conference on Machine Learning (ICML) 40th (2023): 17808–17826.
      • 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.
      • May–June 2023
      • Article

      Analytics for Marketers: When to Rely on Algorithms and When to Trust Your Gut

      By: Fabrizio Fantini and Das Narayandas
      Advanced analytics can help companies solve a host of management problems, including those related to marketing, sales, and supply-chain operations, which can lead to a sustainable competitive advantage. But as more data becomes available and advanced analytics are...  View Details
      Keywords: Analytics and Data Science; Decision Making
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      Fantini, Fabrizio, and Das Narayandas. "Analytics for Marketers: When to Rely on Algorithms and When to Trust Your Gut." Harvard Business Review 101, no. 3 (May–June 2023): 82–91.
      • 2023
      • Working Paper

      Digital Lending and Financial Well-Being: Through the Lens of Mobile Phone Data

      By: AJ Chen, Omri Even-Tov, Jung Koo Kang and Regina Wittenberg-Moerman
      To mitigate information asymmetry about borrowers in developing economies, digital lenders utilize machine-learning algorithms and nontraditional data from borrowers’ mobile devices. Consequently, digital lenders have managed to expand access to credit for millions of...  View Details
      Keywords: Borrowing and Debt; Credit; AI and Machine Learning; Welfare; Well-being; Developing Countries and Economies; Equality and Inequality
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      Chen, AJ, Omri Even-Tov, Jung Koo Kang, and Regina Wittenberg-Moerman. "Digital Lending and Financial Well-Being: Through the Lens of Mobile Phone Data." Harvard Business School Working Paper, No. 23-076, April 2023. (Revised November 2023. SSRN Working Paper Series, November 2023)
      • 2023
      • Working Paper

      Random Distribution Shift in Refugee Placement: Strategies for Building Robust Models

      By: Kirk Bansak, Elisabeth Paulson and Dominik Rothenhäusler
      Algorithmic assignment of refugees and asylum seekers to locations within host countries has gained attention in recent years, with implementations in the U.S. and Switzerland. These approaches use data on past arrivals to generate machine learning models that can...  View Details
      Keywords: AI and Machine Learning; Refugees; Geographic Location; Netherlands
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      Bansak, Kirk, Elisabeth Paulson, and Dominik Rothenhäusler. "Random Distribution Shift in Refugee Placement: Strategies for Building Robust Models." Working Paper, June 2023.
      • June 2020
      • Article

      Real-time Data from Mobile Platforms to Evaluate Sustainable Transportation Infrastructure

      By: Omar Isaac Asensio, Kevin Alvarez, Arielle Dror, Emerson Wenzel, Catharina Hollauer and Sooji Ha
      By displacing gasoline and diesel fuels, electric cars and fleets reduce emissions from the transportation sector, thus offering important public health benefits. However, public confidence in the reliability of charging infrastructure remains a fundamental barrier to...  View Details
      Keywords: Environmental Sustainability; Transportation; Infrastructure; Behavior; AI and Machine Learning; Demand and Consumers
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      Asensio, Omar Isaac, Kevin Alvarez, Arielle Dror, Emerson Wenzel, Catharina Hollauer, and Sooji Ha. "Real-time Data from Mobile Platforms to Evaluate Sustainable Transportation Infrastructure." Nature Sustainability 3, no. 6 (June 2020): 463–471.
      • 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).
      • March 2023
      • Teaching Note

      VideaHealth: Building the AI Factory

      By: Karim R. Lakhani
      Teaching Note for HBS Case No. 621-021. The case “VideaHealth: Building the AI Factory” examines the creation of dental startup VideaHealth (Videa) and the development of its artificial intelligence (AI)-led business strategy through the eyes of founder and CEO Florian...  View Details
      Keywords: AI and Machine Learning; Applications and Software; Business Model; Marketing Strategy; Product Development; Health Industry; Technology Industry
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      Lakhani, Karim R. "VideaHealth: Building the AI Factory." Harvard Business School Teaching Note 623-073, March 2023.
      • March–April 2023
      • Article

      Market Segmentation Trees

      By: Ali Aouad, Adam Elmachtoub, Kris J. Ferreira and Ryan McNellis
      Problem definition: We seek to provide an interpretable framework for segmenting users in a population for personalized decision making. Methodology/results: We propose a general methodology, market segmentation trees (MSTs), for learning market...  View Details
      Keywords: Decision Trees; Computational Advertising; Market Segmentation; Analytics and Data Science; E-commerce; Consumer Behavior; Marketplace Matching; Marketing Channels; Digital Marketing
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      Aouad, Ali, Adam Elmachtoub, Kris J. Ferreira, and Ryan McNellis. "Market Segmentation Trees." Manufacturing & Service Operations Management 25, no. 2 (March–April 2023): 648–667.
      • 2022
      • Working Paper

      Outcome-Driven Dynamic Refugee Assignment with Allocation Balancing

      By: Kirk Bansak and Elisabeth Paulson
      This study proposes two new dynamic assignment algorithms to match refugees and asylum seekers to geographic localities within a host country. The first, currently implemented in a multi-year pilot in Switzerland, seeks to maximize the average predicted employment...  View Details
      Keywords: AI and Machine Learning; Refugees; Mathematical Methods
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      Bansak, Kirk, and Elisabeth Paulson. "Outcome-Driven Dynamic Refugee Assignment with Allocation Balancing." Harvard Business School Working Paper, No. 23-048, January 2022.
      • February 2023
      • Supplement

      Shanty Real Estate: Teaching Note Supplement

      By: Michael Luca
      Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a “comp sheet” of similar properties that have recently sold, and has done a walkthrough....  View Details
      Keywords: Decision Choices and Conditions; Decision Making; Measurement and Metrics; Market Timing
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      Luca, Michael. "Shanty Real Estate: Teaching Note Supplement." Harvard Business School Spreadsheet Supplement 923-715, February 2023.
      • Working Paper

      Group Fairness in Dynamic Refugee Assignment

      By: Daniel Freund, Thodoris Lykouris, Elisabeth Paulson, Bradley Sturt and Wentao Weng
      Ensuring that refugees and asylum seekers thrive (e.g., find employment) in their host countries is a profound humanitarian goal, and a primary driver of employment is the geographic location within a host country to which the refugee or asylum seeker is...  View Details
      Keywords: Refugees; Geographic Location; Mathematical Methods; Employment; Fairness
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      Freund, Daniel, Thodoris Lykouris, Elisabeth Paulson, Bradley Sturt, and Wentao Weng. "Group Fairness in Dynamic Refugee Assignment." Harvard Business School Working Paper, No. 23-047, February 2023.
      • 2022
      • Working Paper

      Human-Computer Interactions in Demand Forecasting and Labor Scheduling Decisions

      By: Caleb Kwon, Ananth Raman and Jorge Tamayo
      We empirically analyze how managerial overrides to a commercial algorithm that forecasts demand and schedules labor affect store performance. We analyze administrative data from a large grocery retailer that utilizes a commercial algorithm to forecast demand and...  View Details
      Keywords: Employees; Human Capital; Performance; Applications and Software; Management Skills; Management Practices and Processes; Retail Industry
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      Kwon, Caleb, Ananth Raman, and Jorge Tamayo. "Human-Computer Interactions in Demand Forecasting and Labor Scheduling Decisions." Working Paper, 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.
      • October 2022
      • Exercise

      Shanty Real Estate: Confidential Information for Homebuyer 1

      By: Michael Luca, Jesse M. Shapiro and Nathan Sun
      Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a “comp sheet” of similar properties that have recently sold, and has done a walkthrough....  View Details
      Keywords: Data-driven Decision-making; Decisions; Negotiation; Bids and Bidding; Valuation; Consumer Behavior; Real Estate Industry
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      Luca, Michael, Jesse M. Shapiro, and Nathan Sun. "Shanty Real Estate: Confidential Information for Homebuyer 1." Harvard Business School Exercise 923-016, October 2022.
      • October 2022
      • Exercise

      Shanty Real Estate: Confidential Information for Homebuyer 2

      By: Michael Luca, Jesse M. Shapiro and Nathan Sun
      Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a “comp sheet” of similar properties that have recently sold, and has done a walkthrough....  View Details
      Keywords: Decision Choices and Conditions; Decision Making; Measurement and Metrics; Market Timing
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      Luca, Michael, Jesse M. Shapiro, and Nathan Sun. "Shanty Real Estate: Confidential Information for Homebuyer 2." Harvard Business School Exercise 923-017, October 2022.
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