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  • 06 Oct 2015
  • First Look

October 6, 2015

themselves and the charity, they respond very similarly to self risk and charity risk. By contrast, when their decisions force tradeoffs between money for themselves and the charity, participants act more averse to charity risk and less averse to self risk. These... View Details
Keywords: Sean Silverthorne
  • November, 2016
  • Article

Fixing Discrimination in Online Marketplaces

By: Ray Fisman and Michael Luca
Online marketplaces such as eBay, Uber, and Airbnb have the potential to reduce racial, gender, and other forms of bias that affect the off-line world. And in the early days of Internet commerce, the relative anonymity of transactions did make it harder for... View Details
Keywords: Prejudice and Bias; Digital Platforms; Internet and the Web; Race; Gender
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    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
    • 28 Feb 2018
    • HBS Seminar

    Kartik Hosanagar, Wharton, University of Pennsylvania

    • October–December 2022
    • Article

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

    By: Mochen Yang, Edward McFowland III, Gordon Burtch and Gediminas Adomavicius
    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, followed... View Details
    Keywords: Machine Learning; Econometric Analysis; Instrumental Variable; Random Forest; Causal Inference; AI and Machine Learning; Forecasting and Prediction
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    • September 2019 (Revised September 2019)
    • Case

    Facebook Fake News in the Post-Truth World

    By: John R. Wells, Carole A. Winkler and Benjamin Weinstock
    In August 2019, Mark Zuckerberg, founder and CEO of Facebook, was surrounded by controversy. The first major storm of protest followed the surprise election of Donald Trump as President of the United States on November 8, 2016; many put the blame at the door of fake... View Details
    Keywords: Facebook; Fake News; Mark Zuckerberg; Donald Trump; Algorithms; Social Networks; Partisanship; Social Media; App Development; Instagram; WhatsApp; Smartphone; Silicon Valley; Office Space; Digital Strategy; Democracy; Entry Barriers; Online Platforms; Controversy; Tencent; Agility; Social Networking; Gaming; Gaming Industry; Computer Games; Mobile Gaming; Messaging; Monetization Strategy; Advertising; Digital Marketing; Business Ventures; Acquisition; Mergers and Acquisitions; Business Growth and Maturation; Business Headquarters; Business Organization; For-Profit Firms; Trends; Communication; Communication Technology; Forms of Communication; Interactive Communication; Interpersonal Communication; Talent and Talent Management; Crime and Corruption; Voting; Demographics; Entertainment; Games, Gaming, and Gambling; Moral Sensibility; Values and Beliefs; Initial Public Offering; Profit; Revenue; Geography; Geographic Location; Global Range; Local Range; Country; Cross-Cultural and Cross-Border Issues; Globalized Firms and Management; Globalized Markets and Industries; Governing Rules, Regulations, and Reforms; Government and Politics; International Relations; National Security; Political Elections; Business History; Recruitment; Selection and Staffing; Information Management; Information Publishing; News; Newspapers; Innovation and Management; Innovation Strategy; Technological Innovation; Knowledge Dissemination; Human Capital; Law; Leadership Development; Leadership Style; Leading Change; Business or Company Management; Crisis Management; Goals and Objectives; Growth and Development Strategy; Growth Management; Management Practices and Processes; Management Style; Management Systems; Management Teams; Managerial Roles; Marketing Channels; Social Marketing; Network Effects; Market Entry and Exit; Digital Platforms; Marketplace Matching; Industry Growth; Industry Structures; Monopoly; Media; Product Development; Service Delivery; Corporate Social Responsibility and Impact; Mission and Purpose; Organizational Change and Adaptation; Organizational Culture; Organizational Structure; Public Ownership; Problems and Challenges; Business and Community Relations; Business and Government Relations; Groups and Teams; Networks; Rank and Position; Opportunities; Behavior; Emotions; Identity; Power and Influence; Prejudice and Bias; Reputation; Social and Collaborative Networks; Status and Position; Trust; Society; Civil Society or Community; Culture; Public Opinion; Social Issues; Societal Protocols; Strategy; Adaptation; Business Strategy; Commercialization; Competition; Competitive Advantage; Competitive Strategy; Corporate Strategy; Customization and Personalization; Diversification; Expansion; Horizontal Integration; Segmentation; Information Technology; Internet and the Web; Mobile and Wireless Technology; Applications and Software; Information Infrastructure; Valuation; Advertising Industry; Communications Industry; Entertainment and Recreation Industry; Information Industry; Information Technology Industry; Journalism and News Industry; Media and Broadcasting Industry; Service Industry; Technology Industry; Telecommunications Industry; Video Game Industry; United States; California; Sunnyvale; Russia
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    • 2023
    • Working Paper

    Insufficiently Justified Disparate Impact: A New Criterion for Subgroup Fairness

    By: Neil Menghani, Edward McFowland III and Daniel B. Neill
    In this paper, we develop a new criterion, "insufficiently justified disparate impact" (IJDI), for assessing whether recommendations (binarized predictions) made by an algorithmic decision support tool are fair. Our novel, utility-based IJDI criterion evaluates false... View Details
    Keywords: AI and Machine Learning; Forecasting and Prediction; Prejudice and Bias
    Related
    • Web

    Publications - Faculty & Research

    The... View Details Keywords: Crowdfunding ; Prejudice and Bias ; Race ; Immigration ; Public Opinion Citation Read Now Related Bai, John (Jianqui), William R. Kerr, Chi Wan, and Alptug Yorulmaz. "Everyone Steps Back? The Widespread... View Details
    • 19 Feb 2019
    • First Look

    New Research and Ideas, February 19, 2019

    algorithm uncovers two distinct behavioral types: "leaders" and "managers." Leaders focus on multi-function, high-level meetings, while managers focus on one-to-one meetings with core functions. Firms with leader CEOs are on average more... View Details
    Keywords: Sean Silverthorne
    • May 2022 (Revised June 2024)
    • 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: AI and Machine Learning; Technological Innovation; Equality and Inequality; Prejudice and Bias; Growth and Development Strategy; Customer Relationship Management; Price; Insurance Industry; Financial Services Industry
    Related
    • 27 Feb 2018
    • First Look

    First Look at New Research and Ideas, February 27, 2018

    entrepreneurs are known to raise higher levels of funding than their female counterparts, but the underlying mechanism for this funding disparity remains contested. Drawing upon Regulatory Focus Theory, we propose that the gap originates with a gender View Details
    Keywords: Sean Silverthorne
    • Web

    Marketing - Faculty & Research

    Marketing Overview Faculty Curriculum Seminars & Conferences Awards & Honors Doctoral Students Featured Publication Frontiers: Can an AI Algorithm Mitigate Racial Economic Inequality? An Analysis in the Context of Airbnb By: Shunyuan... View Details
    • 08 Apr 2014
    • First Look

    First Look: April 8

    Lee, Charles M.C., Paul Ma, and Charles C.Y. Wang Abstract—Applying a "co-search" algorithm to Internet traffic at the SEC's EDGAR website, we develop a novel method for identifying economically related peer firms. Our results... View Details
    Keywords: Sean Silverthorne
    • 10 Feb 2020
    • In Practice

    6 Ways That Emerging Technology Is Disrupting Business Strategy

    Economic Research. 3. Algorithms are changing the pricing game   “Firms are increasingly using pricing algorithms to set prices, especially in online markets. Pricing View Details
    Keywords: by Danielle Kost
    • 19 Jan 2023
    • Research & Ideas

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

    When an algorithm recommends ways to improve business outcomes, do employees trust it? Conventional wisdom suggests that understanding the inner workings of artificial intelligence (AI) can raise confidence in such programs. Yet, new... View Details
    Keywords: by Rachel Layne
    • 07 Feb 2022
    • Research & Ideas

    Digital Transformation: A New Roadmap for Success

    algorithms can lead to unintended bias that harms certain employees and customers, and the company’s reputation (a bias story can go viral on social media within minutes). 5.... View Details
    Keywords: by Linda A. Hill, Ann Le Cam, Sunand Menon, and Emily Tedards
    • 01 Dec 2023
    • News

    Thinking Ahead

    As we wind down 2023, there’s talk everywhere of generative AI and how it will fundamentally alter the world as we know it; but how does that translate for your corner of the business world? Is TikTok something you need to take seriously? (Is it time to dance?) We... View Details
    Keywords: Julia Hanna; Illustrations by Chris Gash; News, Library, Internet, and Other Services; Information
    • 12 Apr 2022
    • Research & Ideas

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

    we had no real idea what would prevail. So, this is where the scientific mystery lies,” he says. Applying dating algorithms to other industries, cautiously Platonic platforms could follow similar, industry-appropriate revelation models.... View Details
    Keywords: by Kara Baskin
    • 28 Jul 2020
    • Research & Ideas

    Racism and Digital Design: How Online Platforms Can Thwart Discrimination

    inclusive design choices in a forthcoming article in the journal Marketing Intelligence Review. What follows is a condensed version: Build awareness. Digital platform builders must recognize how their design choices and algorithms can... View Details
    Keywords: by Kristen Senz; Travel; Entertainment & Recreation; Service
    • Web

    2023 Reunion Presentations - Alumni

    session will be published soon. CASE STUDY: The Dark Side of AI: Algorithmic Bias and Discrimination Associate Professor Ayelet Israeli + More Info – Less Info This modified case discussion does not require... View Details