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      • Faculty Publications  (90)

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      • 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
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      Pawelczyk, Martin, Himabindu Lakkaraju, and Seth Neel. "On the Privacy Risks of Algorithmic Recourse." International Conference on Artificial Intelligence and Statistics (AISTATS) (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
      Citation
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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
      Citation
      Purchase
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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.
      • October 2022
      • Exercise

      Shanty Real Estate: Confidential Information for Homebuyer 3

      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
      Citation
      Purchase
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      Luca, Michael, Jesse M. Shapiro, and Nathan Sun. "Shanty Real Estate: Confidential Information for Homebuyer 3." Harvard Business School Exercise 923-018, October 2022.
      • October 2022
      • Exercise

      Shanty Real Estate: Confidential Information for iBuyer 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: Algorithm; Decision Choices and Conditions; Decision Making; Measurement and Metrics; Market Timing
      Citation
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      Luca, Michael, Jesse M. Shapiro, and Nathan Sun. "Shanty Real Estate: Confidential Information for iBuyer 1." Harvard Business School Exercise 923-019, October 2022.
      • October 2022
      • Exercise

      Shanty Real Estate: Confidential Information for iBuyer 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; Market Timing; Measurement and Metrics
      Citation
      Purchase
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      Luca, Michael, Jesse M. Shapiro, and Nathan Sun. "Shanty Real Estate: Confidential Information for iBuyer 2." Harvard Business School Exercise 923-020, October 2022.
      • October 2022
      • Exercise

      Shanty Real Estate: Confidential Information for iBuyer 3

      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: Algorithm; Decision Choices and Conditions; Decision Making; Measurement and Metrics; Market Timing
      Citation
      Purchase
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      Luca, Michael, Jesse M. Shapiro, and Nathan Sun. "Shanty Real Estate: Confidential Information for iBuyer 3." Harvard Business School Exercise 923-021, October 2022.
      • October 2022
      • Exercise

      Shanty Real Estate: Updated Confidential Information for Homebuyer

      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: Algorithm; Decision Choices and Conditions; Decision Making; Market Timing; Measurement and Metrics
      Citation
      Purchase
      Related
      Luca, Michael, Jesse M. Shapiro, and Nathan Sun. "Shanty Real Estate: Updated Confidential Information for Homebuyer." Harvard Business School Exercise 923-022, October 2022.
      • October 2022
      • Exercise

      Shanty Real Estate: Updated Confidential Information for iBuyer

      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: Algorithm; Decision Choices and Conditions; Measurement and Metrics; Market Timing; Decision Making
      Citation
      Purchase
      Related
      Luca, Michael, Jesse M. Shapiro, and Nathan Sun. "Shanty Real Estate: Updated Confidential Information for iBuyer." Harvard Business School Exercise 923-023, October 2022.
      • 2022
      • Working Paper

      Price Monitoring and Response: A Supply Side Characterization

      By: Ayelet Israeli and Eric Anderson
      With the growth of e-commerce and increasing price transparency, there has been tremendous interest in the adoption and competitive consequences of algorithmic pricing. A premise of algorithmic pricing is that firms monitor competitors' prices and then their pricing...  View Details
      Keywords: Algorithmic Pricing; Ecommerce; Price Monitoring; Price; Competition; Retail Industry
      Citation
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      Israeli, Ayelet, and Eric Anderson. "Price Monitoring and Response: A Supply Side Characterization." Working Paper, October 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
      Citation
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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
      • Supplement

      Zalora: Data-Driven Pricing Recommendations

      By: Ayelet Israeli
      This exercise can be used in conjunction with the main case "Zalora: Data-Driven Pricing" to facilitate class discussion without requiring data analysis from the students. Instead, the exercise presents reports that were created by the data science team to answer the...  View Details
      Keywords: Pricing; Pricing Algorithms; Dynamic Pricing; Ecommerce; Pricing Strategy; Pricing And Revenue Management; Apparel; Singapore; Startup; Demand Estimation; Data Analysis; Data Analytics; Exercise; Price; Internet and the Web; Apparel and Accessories Industry; Retail Industry; Fashion Industry; Singapore
      Citation
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      Israeli, Ayelet. "Zalora: Data-Driven Pricing Recommendations." Harvard Business School Supplement 523-032, August 2022.
      • 2022
      • Working Paper

      Dynamic Pricing and Demand Volatility: Evidence from Restaurant Food Delivery

      By: Alexander J. MacKay, Dennis Svartbäck and Anders G. Ekholm
      Pricing technology that allows firms to rapidly adjust prices has two potential benefits. Prices can respond more rapidly to demand shocks, leading to higher revenues. On the other hand, time-varying prices can be used to smooth out demand across periods, reducing...  View Details
      Keywords: Pricing Algorithms; Dynamic Pricing; Demand Volatility; Delivery Services
      Citation
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      MacKay, Alexander J., Dennis Svartbäck, and Anders G. Ekholm. "Dynamic Pricing and Demand Volatility: Evidence from Restaurant Food Delivery." Harvard Business School Working Paper, No. 23-007, July 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
      Citation
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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.
      • Article

      Eliminating Unintended Bias in Personalized Policies Using Bias-Eliminating Adapted Trees (BEAT)

      By: Eva Ascarza and Ayelet Israeli

      An inherent risk of algorithmic personalization is disproportionate targeting of individuals from certain groups (or demographic characteristics such as gender or race), even when the decision maker does not intend to discriminate based on those “protected”...  View Details

      Keywords: Algorithm Bias; Personalization; Targeting; Generalized Random Forests (GRF); Discrimination; Customization and Personalization; Decision Making; Fairness; Mathematical Methods
      Citation
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      Ascarza, Eva, and Ayelet Israeli. "Eliminating Unintended Bias in Personalized Policies Using Bias-Eliminating Adapted Trees (BEAT)." e2115126119. Proceedings of the National Academy of Sciences 119, no. 11 (March 8, 2022).
      • March 2022
      • Article

      Learning to Rank an Assortment of Products

      By: Kris Ferreira, Sunanda Parthasarathy and Shreyas Sekar
      We consider the product ranking challenge that online retailers face when their customers typically behave as “window shoppers”: they form an impression of the assortment after browsing products ranked in the initial positions and then decide whether to continue...  View Details
      Keywords: Online Learning; Product Ranking; Assortment Optimization; Learning; Internet and the Web; Product Marketing; Consumer Behavior; E-commerce
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      Ferreira, Kris, Sunanda Parthasarathy, and Shreyas Sekar. "Learning to Rank an Assortment of Products." Management Science 68, no. 3 (March 2022): 1828–1848.
      • Article

      Adaptive Machine Unlearning

      By: Varun Gupta, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi and Chris Waites
      Data deletion algorithms aim to remove the influence of deleted data points from trained models at a cheaper computational cost than fully retraining those models. However, for sequences of deletions, most prior work in the non-convex setting gives valid guarantees...  View Details
      Keywords: Machine Learning; AI and Machine Learning
      Citation
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      Gupta, Varun, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, and Chris Waites. "Adaptive Machine Unlearning." Advances in Neural Information Processing Systems (NeurIPS) 34 (2021).
      • Article

      Counterfactual Explanations Can Be Manipulated

      By: Dylan Slack, Sophie Hilgard, Himabindu Lakkaraju and Sameer Singh
      Counterfactual explanations are useful for both generating recourse and auditing fairness between groups. We seek to understand whether adversaries can manipulate counterfactual explanations in an algorithmic recourse setting: if counterfactual explanations indicate...  View Details
      Keywords: Machine Learning Models; Counterfactual Explanations
      Citation
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      Slack, Dylan, Sophie Hilgard, Himabindu Lakkaraju, and Sameer Singh. "Counterfactual Explanations Can Be Manipulated." Advances in Neural Information Processing Systems (NeurIPS) 34 (2021).
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