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Publications

Publications

Filter Results: (69) Arrow Down
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  • All HBS Web  (69)
    • News  (10)
    • Research  (49)
    • Events  (1)
  • Faculty Publications  (31)

Show Results For

  • All HBS Web  (69)
    • News  (10)
    • Research  (49)
    • Events  (1)
  • Faculty Publications  (31)
← Page 2 of 69 Results →
  • 23 May 2023
  • Research & Ideas

Face Value: Do Certain Physical Features Help People Get Ahead?

empirically predicted with a machine learning model, suggests work by Shunyuan Zhang, an assistant professor at Harvard Business School, and collaborators. “Our research represents the first empirical attempt to characterize the... View Details
Keywords: by Kara Baskin
  • 12 Apr 2022
  • Research & Ideas

Swiping Right: How Data Helped This Online Dating Site Make More Matches

some estimates, with players such as Bumble, Tinder, and OKCupid vying to help people find love. While McFowland is not a dating expert, his work in machine learning and social sciences examines the efficacy of how people interact in... View Details
Keywords: by Kara Baskin
  • 11 Apr 2023
  • Research & Ideas

Is Amazon a Retailer, a Tech Firm, or a Media Company? How AI Can Help Investors Decide

industry lines as companies increasingly bring seemingly unrelated business lines together in unconventional ways. New research by Awada, Harvard Business School Professor Suraj Srinivasan, and doctoral student Paul J. Hamilton harnesses View Details
Keywords: by Danielle Kost; Consumer Products; Real Estate; Financial Services; Retail
  • Teaching Interest

Overview

Paul is primarily interested in teaching data science to management students through the case method. This includes technical topics (programming and statistics) as well as higher-level management issues (digital transformation, data governance, etc.) As a research... View Details
Keywords: A/B Testing; AI; AI Algorithms; AI Creativity; Algorithm; Algorithm Bias; Algorithmic Bias; Algorithmic Fairness; Algorithms; Analytics; Application Program Interface; Artificial Intelligence; Causality; Causal Inference; Computing; Computers; Data Analysis; Data Analytics; Data Architecture; Data As A Service; Data Centers; Data Governance; Data Labeling; Data Management; Data Manipulation; Data Mining; Data Ownership; Data Privacy; Data Protection; Data Science; Data Science And Analytics Management; Data Scientists; Data Security; Data Sharing; Data Strategy; Data Visualization; Database; Data-driven Decision-making; Data-driven Management; Data-driven Operations; Datathon; Economics Of AI; Economics Of Innovation; Economics Of Information System; Economics Of Science; Forecast; Forecast Accuracy; Forecasting; Forecasting And Prediction; Information Technology; Machine Learning; Machine Learning Models; Prediction; Prediction Error; Predictive Analytics; Predictive Models; Analysis; AI and Machine Learning; Analytics and Data Science; Applications and Software; Digital Transformation; Information Management; Digital Strategy; Technology Adoption
  • 19 Jan 2023
  • Research & Ideas

What Makes Employees Trust (vs. Second-Guess) AI?

products were grouped in 241 “style-colors'' and sizes. When the allocators received a recommendation from an interpretable algorithm, they often overruled it based on their own intuition. But when the same allocators had a recommendation from a similarly accurate... View Details
Keywords: by Rachel Layne
  • 26 Jul 2022
  • Research & Ideas

Burgers with Bugs? What Happens When Restaurants Ignore Online Reviews

reviews helps consumers choose cleaner restaurants, which is a pretty robust finding." Harvard Business School Assistant Professor Chiara Farronato and Georgios Zervas, an associate professor at Boston University, used machine learning to... View Details
Keywords: by Kara Baskin; Entertainment & Recreation; Food & Beverage; Retail

    Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem

    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,... View Details
    • May 9, 2023
    • Article

    8 Questions About Using AI Responsibly, Answered

    By: Tsedal Neeley
    Generative AI tools are poised to change the way every business operates. As your own organization begins strategizing which to use, and how, operational and ethical considerations are inevitable. This article delves into eight of them, including how your organization... View Details
    Keywords: AI and Machine Learning; Organizational Change and Adaptation; Prejudice and Bias; Ethics
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    Neeley, Tsedal. "8 Questions About Using AI Responsibly, Answered." Harvard Business Review (website) (May 9, 2023).
    • 03 Apr 2025
    • HBS Seminar

    Ziad Obermeyer, UC Berkeley School of Public Health

    • 19 Feb 2019
    • First Look

    New Research and Ideas, February 19, 2019

    forthcoming Journal of Political Economy CEO Behavior and Firm Performance By: Bandiera, Oriana, Stephen Hansen, Andrea Prat, and Raffaella Sadun Abstract— We measure the behavior of 1,114 CEOs in six countries parsing granular CEO diary data through an unsupervised... View Details
    Keywords: Sean Silverthorne
    • 2023
    • Working Paper

    The Limits of Algorithmic Measures of Race in Studies of Outcome Disparities

    By: David S. Scharfstein and Sergey Chernenko
    We show that the use of algorithms to predict race has significant limitations in measuring and understanding the sources of racial disparities in finance, economics, and other contexts. First, we derive theoretically the direction and magnitude of measurement bias in... View Details
    Keywords: Racial Disparity; Paycheck Protection Program; Measurement Error; AI and Machine Learning; Race; Measurement and Metrics; Equality and Inequality; Prejudice and Bias; Forecasting and Prediction; Outcome or Result
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    Scharfstein, David S., and Sergey Chernenko. "The Limits of Algorithmic Measures of Race in Studies of Outcome Disparities." Working Paper, April 2023.
    • 08 May 2018
    • First Look

    First Look at New Research and Ideas, May 8, 2018

    unexpected networking opportunities, generating a tight community of German businesspeople in India. Publisher's link: https://www.hbs.edu/faculty/Pages/item.aspx?num=54465 How Scheduling Can Bias Quality Assessment: Evidence from Food... View Details
    Keywords: Sean Silverthorne
    • 2020
    • Working Paper

    (When) Does Appearance Matter? Evidence from a Randomized Controlled Trial

    By: Prithwiraj Choudhury, Tarun Khanna, Christos A. Makridis and Subhradip Sarker
    While there is evidence about labor market discrimination based on race, religion, and gender, we know little about whether physical appearance leads to discrimination in labor market outcomes. We deploy a randomized experiment on 1,000 respondents in India between... View Details
    Keywords: Behavioral Economics; Coronavirus; Discrimination; Homophily; Labor Market Mobility; Limited Attention; Resumes; Personal Characteristics; Prejudice and Bias
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    Choudhury, Prithwiraj, Tarun Khanna, Christos A. Makridis, and Subhradip Sarker. "(When) Does Appearance Matter? Evidence from a Randomized Controlled Trial." Harvard Business School Working Paper, No. 21-038, September 2020.
    • 26 Apr 2023
    • In Practice

    Is AI Coming for Your Job?

    will be displaced in large numbers. Those job losses will be partially offset by job gains for machine learning specialists and emerging jobs like prompt engineers. But, once companies learn how to exploit generative AI, we can anticipate... View Details
    Keywords: by Kristen Senz; Technology
    • Forthcoming
    • Article

    Human-Algorithm Collaboration with Private Information: Naïve Advice Weighting Behavior and Mitigation

    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: AI and Machine Learning; Analytics and Data Science; Forecasting and Prediction; Digital Marketing
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    Balakrishnan, Maya, Kris Ferreira, and Jordan Tong. "Human-Algorithm Collaboration with Private Information: Naïve Advice Weighting Behavior and Mitigation." Management Science (forthcoming). (Pre-published online March 24, 2025.)
    • 06 Jun 2017
    • First Look

    First Look at New Research and Ideas: June 6, 2017

    reallocation accounts for the majority of aggregate productivity gains, suggesting that ignoring this channel could lead to substantial bias in understanding the nature of gains from multinational production. Publisher's link:... View Details
    Keywords: Sean Silverthorne
    • 17 Jun 2014
    • First Look

    First Look: June 17

    feedback influence order quantities. We find that the portion of mismatch cost due to adjustment behavior exceeds the portion of mismatch cost due to level behavior in three out of four conditions. Observation bias is studied through... View Details
    Keywords: Sean Silverthorne
    • 08 Apr 2014
    • First Look

    First Look: April 8

    By: Hałaburda, Hanna, and Felix Oberholzer-Gee Abstract—The value of many products and services rises or falls with the number of customers using them; the fewer fax machines in use, the less important it is to have one. These network... View Details
    Keywords: Sean Silverthorne
    • 2025
    • Working Paper

    Warnings and Endorsements: Improving Human-AI Collaboration in the Presence of Outliers

    By: Matthew DosSantos DiSorbo, Kris Ferreira, Maya Balakrishnan and Jordan Tong
    Problem definition: While artificial intelligence (AI) algorithms may perform well on data that are representative of the training set (inliers), they may err when extrapolating on non-representative data (outliers). How can humans and algorithms work together to make... View Details
    Keywords: AI and Machine Learning; Decision Choices and Conditions
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    DosSantos DiSorbo, Matthew, Kris Ferreira, Maya Balakrishnan, and Jordan Tong. "Warnings and Endorsements: Improving Human-AI Collaboration in the Presence of Outliers." Working Paper, May 2025.
    • Web

    Technology & Innovation - Faculty & Research

    and unfunded firms. Because randomization of the sample was not feasible, we address endogeneity around selection bias using a sample of qualitatively similar firms based on a funding decision score. This allows us to observe the local... View Details
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