Skip to main navigation Skip to main content
info(at)avldigital.remove-this.de
Deutsch English
Narrow Search
Search narrowed by
Last searches

Results for *

Displaying results 1 to 2 of 2.

  1. Smiles in profiles: improving fairness and efficiency using estimates of user preferences in online marketplaces
    Author: Athey, Susan; Karlan, Dean; Palikot, Emil; Yuan, Yuan
    Published: [2022]
    Publisher:  Stanford Institute for Economic Policy Research (SIEPR), Stanford, CA

    Access:
    Verlag (kostenfrei)
    Verlag (kostenfrei)

    Location:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signature:
    Keine Rechte
    Inter-library loan:
    No inter-library loan
    Export to reference management software   RIS file
      BibTeX file
    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Series: Working paper / Stanford Institute for Economic Policy Research (SIEPR) ; no. 22, 22 (Sept, 2022)
    Subjects: Digitale Plattform; Electronic Commerce; Verbraucherkredit; Künstliche Intelligenz; Matching; Konsumentenpräferenzen; Werbepsychologie; Experiment; Discrimination; Computer vision algorithms; Digital platforms; Matching markets; Experimental methods
    Scope: 1 Online-Ressource (circa 57 Seiten), Illustrationen
  2. Smiles in Profiles
    Improving Fairness and Efficiency Using Estimates of User Preferences in Online Marketplaces
    Author: Athey, Susan; Karlan, Dean; Palikot, Emil; Yuan, Yuan
    Published: November 2022
    Publisher:  National Bureau of Economic Research, Cambridge, Mass

    Online platforms often face challenges being both fair (i.e., non-discriminatory) and efficient (i.e., maximizing revenue). Using computer vision algorithms and observational data from a microlending marketplace, we find that choices made by... more

    Access:
    Verlag (kostenfrei)
    Resolving-System (kostenfrei)

    Location:
    Sächsische Landesbibliothek - Staats- und Universitätsbibliothek Dresden
    Inter-library loan:
    No inter-library loan

    Location:
    Universitätsbibliothek Freiburg
    Inter-library loan:
    No inter-library loan

    Location:
    Helmut-Schmidt-Universität, Universität der Bundeswehr Hamburg, Universitätsbibliothek
    Inter-library loan:
    No inter-library loan

    Location:
    Staats- und Universitätsbibliothek Hamburg Carl von Ossietzky
    Inter-library loan:
    No inter-library loan

    Location:
    Technische Informationsbibliothek (TIB) / Leibniz-Informationszentrum Technik und Naturwissenschaften und Universitätsbibliothek
    Inter-library loan:
    No inter-library loan

    Location:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Inter-library loan:
    No inter-library loan

     

    Online platforms often face challenges being both fair (i.e., non-discriminatory) and efficient (i.e., maximizing revenue). Using computer vision algorithms and observational data from a microlending marketplace, we find that choices made by borrowers creating online profiles impact both of these objectives. We further support this conclusion with a web-based randomized survey experiment. In the experiment, we create profile images using Generative Adversarial Networks that differ in a specific feature and estimate its impact on lender demand. We then counterfactually evaluate alternative platform policies and identify particular approaches to influencing the changeable profile photo features that can ameliorate the fairness-efficiency tension

     
    Export to reference management software   RIS file
      BibTeX file
    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Series: NBER working paper series ; no. w30633
    Subjects: Digitale Plattform; Electronic Commerce; Verbraucherkredit; Künstliche Intelligenz; Matching; Konsumentenpräferenzen; Werbepsychologie; Experiment; General; General; General; Economic Development
    Scope: 1 Online-Ressource, illustrations (black and white)
    Notes:

    Hardcopy version available to institutional subscribers