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      • Faculty Publications  (147)

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      • May 2022
      • Case

      LOOP: Driving Change in Auto Insurance Pricing

      By: Elie Ofek and Alicia Dadlani
      John Henry and Carey Anne Nadeau, co-founders and co-CEOs of LOOP, an insurtech startup based in Austin, Texas, were on a mission to modernize the archaic $250 billion automobile insurance market. They sought to create equitably priced insurance by eliminating pricing...  View Details
      Keywords: Insurance Industry; Financial Services Industry
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      Ofek, Elie, and Alicia Dadlani. "LOOP: Driving Change in Auto Insurance Pricing." Harvard Business School Case 522-073, May 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
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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; E-commerce; Learning; Internet and the Web; Product Marketing; Consumer Behavior
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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.
      • January–February 2022
      • Article

      Algorithm-Augmented Work and Domain Experience: The Countervailing Forces of Ability and Aversion

      By: Ryan Allen and Prithwiraj Choudhury
      How does a knowledge worker’s level of domain experience affect their algorithm-augmented work performance? We propose and test theoretical predictions that domain experience has countervailing effects on algorithm-augmented performance: on one hand, domain experience...  View Details
      Keywords: Automation; Domain Experience; Algorithmic Aversion; Experts; Algorithms; Machine Learning; Future Of Work; Employees; Experience and Expertise; Decision Making; Performance
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      Allen, Ryan, and Prithwiraj Choudhury. "Algorithm-Augmented Work and Domain Experience: The Countervailing Forces of Ability and Aversion." Organization Science 33, no. 1 (January–February 2022): 149–169. ("Best PhD Student Paper" at SMS conference 2020.)
      • 2021
      • Working Paper

      Dynamic Pricing Algorithms, Consumer Harm, and Regulatory Response

      By: Alexander J. MacKay and Samuel Weinstein
      Pricing algorithms are rapidly transforming markets, from ride-sharing apps, to air travel, to online retail. Regulators and scholars have watched this development with a wary eye. Their focus so far has been on the potential for pricing algorithms to facilitate...  View Details
      Keywords: Competition Policy; Regulation; Algorithmic Pricing; Dynamic Pricing; Law And Economics; Law And Regulation; Consumer Protection; Competition; Policy; Price; Governing Rules, Regulations, and Reforms; Economics
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      MacKay, Alexander J., and Samuel Weinstein. "Dynamic Pricing Algorithms, Consumer Harm, and Regulatory Response." Harvard Business School Working Paper, No. 22-050, January 2022. (Direct download.)
      • 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
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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
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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).
      • December 1, 2021
      • Article

      Do You Know How Your Teams Get Work Done?

      By: Rohan Narayana Murty, Rajath B. Das, Scott Duke Kominers, Arjun Narayan, Suraj Srinivasan, Tarun Khanna and Kartik Hosanagar
      In a research study at four Fortune 500 companies, when managers were asked about their teams’ work, on average they either did not know or could not remember 60% of the work their teams do. This is a major problem because it can lead to unrealistic digital...  View Details
      Keywords: Leading Teams; Work Recall Gap; Machine Learning; Algorithms; Groups and Teams; Management; Technological Innovation
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      Murty, Rohan Narayana, Rajath B. Das, Scott Duke Kominers, Arjun Narayan, Suraj Srinivasan, Tarun Khanna, and Kartik Hosanagar. "Do You Know How Your Teams Get Work Done?" Harvard Business Review Digital Articles (December 1, 2021).
      • 2021
      • Book

      The Future of Executive Development

      By: Mihnea C Moldoveanu and Das Narayandas
      Executive development programs have entered a period of rapid transformation, driven by digital disruption and a widening gap between the skills that participants and their organizations demand and those provided by their executive programs. This work delves into the...  View Details
      Keywords: Executive Education; Leadership Development; Management Skills; Education Industry
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      Moldoveanu, Mihnea C., and Das Narayandas. The Future of Executive Development. Stanford, CA: Stanford Business Books, 2021.
      • November 2021 (Revised December 2021)
      • Supplement

      PittaRosso (B): Human and Machine Learning

      By: Ayelet Israeli
      This case supplements the "PittaRosso: Artificial Intelligence-Driven Pricing and Promotion" case, and provides major highlights on what happened at the company since the first case.  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 (B): Human and Machine Learning." Harvard Business School Supplement 522-047, November 2021. (Revised December 2021.)
      • October 2021 (Revised March 2022)
      • Supplement

      PittaRosso: Artificial Intelligence-Driven Pricing and Promotion

      By: Ayelet Israeli and Fabrizio Fantini
      PittaRosso, a traditional Italian shoe retailer, is implementing an AI system to provide pricing and promotion recommendations. The system allows them to implement changes that would affect both the top of funnel and bottom of funnel activities for the company: once...  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; Retail Industry; Italy
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      Israeli, Ayelet, and Fabrizio Fantini. "PittaRosso: Artificial Intelligence-Driven Pricing and Promotion." Harvard Business School Spreadsheet Supplement 522-710, October 2021. (Revised March 2022.)
      • October 2021 (Revised March 2022)
      • Case

      PittaRosso: Artificial Intelligence-Driven Pricing and Promotion

      By: Ayelet Israeli
      PittaRosso, a traditional Italian shoe retailer, is implementing an AI system to provide pricing and promotion recommendations. The system allows them to implement changes that would affect both the top of funnel and bottom of funnel activities for the company: once...  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; AI; 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 Case 522-046, October 2021. (Revised March 2022.)
      • September–October 2021
      • Article

      Frontiers: Can an AI Algorithm Mitigate Racial Economic Inequality? An Analysis in the Context of Airbnb

      By: Shunyuan Zhang, Nitin Mehta, Param Singh and Kannan Srinivasan
      We study the effect of Airbnb’s smart-pricing algorithm on the racial disparity in the daily revenue earned by Airbnb hosts. Our empirical strategy exploits Airbnb’s introduction of the algorithm and its voluntary adoption by hosts as a quasi-natural experiment. Among...  View Details
      Keywords: Smart Pricing; Pricing Algorithm; Machine Bias; Discrimination; Racial Disparity; Social Inequality; Airbnb Revenue; Revenue; Race; Equality and Inequality; Prejudice and Bias; Price; Mathematical Methods; Accommodations Industry
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      Zhang, Shunyuan, Nitin Mehta, Param Singh, and Kannan Srinivasan. "Frontiers: Can an AI Algorithm Mitigate Racial Economic Inequality? An Analysis in the Context of Airbnb." Marketing Science 40, no. 5 (September–October 2021): 813–820.
      • 2021
      • Working Paper

      Invisible Primes: Fintech Lending with Alternative Data

      By: Marco Di Maggio, Dimuthu Ratnadiwakara and Don Carmichael
      We exploit anonymized administrative data provided by a major fintech platform to investigate whether using alternative data to assess borrowers’ creditworthiness results in broader credit access. Comparing actual outcomes of the fintech platform’s model to...  View Details
      Keywords: Fintech Lending; Alternative Data; Machine Learning; Algorithm Bias; Finance; Information Technology; Financing and Loans; Analytics and Data Science; Credit
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      Di Maggio, Marco, Dimuthu Ratnadiwakara, and Don Carmichael. "Invisible Primes: Fintech Lending with Alternative Data." Harvard Business School Working Paper, No. 22-024, October 2021.
      • September 24, 2021
      • Article

      A Labor Movement for the Platform Economy

      By: Li Jin, Scott Duke Kominers and Lila Shroff
      Platforms are fundamentally changing the contract between workers and companies—and the workers and creatives that create value for platform companies, and rely on platforms for their livelihoods, often have little power when it comes to getting their concerns...  View Details
      Keywords: Digital Platforms; Gig Workers; Decentralized Collective Action; Internet and the Web; Labor; Labor and Management Relations
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      Jin, Li, Scott Duke Kominers, and Lila Shroff. "A Labor Movement for the Platform Economy." Harvard Business Review Digital Articles (September 24, 2021).
      • September 17, 2021
      • Article

      AI Can Help Address Inequity—If Companies Earn Users' Trust

      By: Shunyuan Zhang, Kannan Srinivasan, Param Singh and Nitin Mehta
      While companies may spend a lot of time testing models before launch, many spend too little time considering how they will work in the wild. In particular, they fail to fully consider how rates of adoption can warp developers’ intent. For instance, Airbnb launched a...  View Details
      Keywords: Artificial Intelligence; Algorithmic Bias; Technological Innovation; Perception; Diversity; Equality and Inequality; Trust; AI and Machine Learning
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      Zhang, Shunyuan, Kannan Srinivasan, Param Singh, and Nitin Mehta. "AI Can Help Address Inequity—If Companies Earn Users' Trust." Harvard Business Review Digital Articles (September 17, 2021).
      • 2021
      • Working Paper

      The Demand for Executive Skills

      By: Stephen Hansen, Raffaella Sadun, Tejas Ramdas and Joseph B. Fuller
      We use a unique corpus of job descriptions for C-suite positions to document skills requirements in top managerial occupations across a large sample of firms. A novel algorithm maps the text of each executive search into six separate skill clusters reflecting...  View Details
      Keywords: C-Suite; Jobs and Positions; Competency and Skills; Management Skills; Job Search; Job Design and Levels
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      Hansen, Stephen, Raffaella Sadun, Tejas Ramdas, and Joseph B. Fuller. "The Demand for Executive Skills." Harvard Business School Working Paper, No. 21-133, June 2021.
      • Article

      The Pitfalls of Pricing Algorithms: Be Mindful of How They Can Hurt Your Brand

      By: Marco Bertini and Oded Koenigsberg
      More and more companies are relying on pricing algorithms to maximize profits. The use of artificial intelligence and machine learning enables real-time price adjustments based on supply and demand, competitors’ activities, delivery schedules, and so forth. But...  View Details
      Keywords: Algorithmic Pricing; Dynamic Pricing; Price; Change; Information Technology; Brands and Branding; Perception; Consumer Behavior
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      Bertini, Marco, and Oded Koenigsberg. "The Pitfalls of Pricing Algorithms: Be Mindful of How They Can Hurt Your Brand." Harvard Business Review 99, no. 5 (September–October 2021): 74–83.
      • August 2021 (Revised April 2022)
      • Case

      Intenseye: Powering Workplace Health and Safety with AI

      By: Michael W. Toffel and Youssef Abdel Aal
      Intenseye was a Turkey-based technology startup that deployed machine learning algorithms to workplace camera feeds in order to identify unsafe worker actions and unsafe working conditions, in order to help improve worker safety. The case describes how Intenseye’s...  View Details
      Keywords: Privacy; Product Development; Operations; Technological Innovation; Value Creation; Production; Distribution; Safety; Risk and Uncertainty; Technology Industry; Manufacturing Industry; Distribution Industry; Turkey; Middle East; United States
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      Toffel, Michael W., and Youssef Abdel Aal. "Intenseye: Powering Workplace Health and Safety with AI." Harvard Business School Case 622-037, August 2021. (Revised April 2022.)
      • Article

      Learning Models for Actionable Recourse

      By: Alexis Ross, Himabindu Lakkaraju and Osbert Bastani
      As machine learning models are increasingly deployed in high-stakes domains such as legal and financial decision-making, there has been growing interest in post-hoc methods for generating counterfactual explanations. Such explanations provide individuals adversely...  View Details
      Keywords: Machine Learning Models; Recourse; Algorithm; Mathematical Methods
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      Ross, Alexis, Himabindu Lakkaraju, and Osbert Bastani. "Learning Models for Actionable Recourse." Advances in Neural Information Processing Systems (NeurIPS) 34 (2021).
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