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Data and Code for: The Managerial Effects of Algorithmic Fairness Activism

Resource Type
Dataset : survey data
  • Cowgill, Bo (Columbia Business School)
  • Dell'Acqua, Fabrizio (Columbia Business School)
  • Matz, Sandra (Columbia Business School)
Publication Date
Free Keywords
machine learning; ethics; political activism; social activism; activism; framing
  • Abstract

    How do ethical arguments affect AI adoption in business? We randomly expose business decision-makers to arguments used in AI fairness activism. Arguments emphasizing the inescapability of algorithmic bias lead managers to abandon AI for manual review by humans and report greater expectations about lawsuits and negative PR. These effects persist even when AI lowers gender and racial disparities and when engineering investments to address AI fairness are feasible. Emphasis on status quo comparisons yields opposite effects. We also measure the effects of "scientific veneer" in AI ethics arguments. Scientific veneer changes managerial behavior but does not asymmetrically benefit favorable (versus critical) AI activism.
Temporal Coverage
  • 2020-01-09 / 2020-01-10
    Time Period: Thu Jan 09 00:00:00 EST 2020--Fri Jan 10 00:00:00 EST 2020 (January 2020)
This study is freely available to the general public via web download.
  • Is version of
    DOI: 10.3886/E120752
  • Cowgill, Bo, Fabrizio Dell’Acqua, and Sandra Matz. “The Managerial Effects of Algorithmic Fairness Activism.” AEA Papers and Proceedings 110 (May 2020): 85–90.
    • ID: 10.1257/pandp.20201035 (DOI)

Update Metadata: 2020-08-27 | Issue Number: 1 | Registration Date: 2020-08-27

Cowgill, Bo; Dell'Acqua, Fabrizio; Matz, Sandra (2020): Data and Code for: The Managerial Effects of Algorithmic Fairness Activism. Version: 1. ICPSR - Interuniversity Consortium for Political and Social Research. Dataset.