Targeting vulnerable populations: The ethical implications of data mining, automated prediction, and focused marketing

Published date01 June 2021
AuthorGerard A. Callanan,David F. Perri,Sandra M. Tomkowicz
Date01 June 2021
DOIhttp://doi.org/10.1111/basr.12233
ORIGINAL ARTICLE
Targeting vulnerable populations: The ethical
implications of data mining, automated
prediction, and focused marketing
Gerard A. Callanan | David F. Perri | Sandra M. Tomkowicz
Department of Management, West
Chester University, West Chester,
Pennsylvania, USA
Correspondence
David F. Perri, Department of
Management, West Chester University,
Business Center (BPMC), Office
446, West Chester, PA 19383, USA.
Email: dperri@wcupa.edu
Abstract
This article presents an overview of the ethically
questionable business strategies of data mining and
automated prediction with respect to vulnerable
populations of consumers. It first provides a summary
of the privacy and ethical issues that arise when
high-tech internet-based organizations collect and
analyze large quantities of personal data from the users
of their systems and how these data are analyzed to
make assumptions and projections on individual
behaviors. It then discusses how these actions might
be technically legal but violate established moral
and ethical standards. The paper also highlights the
role that governments can play in limiting the ability
of large tech-based companies to mine personal
information for commercial application. The article
closes with suggestions for future research.
KEYWORDS
automated prediction, business ethics, computational
inference, data mining
1|INTRODUCTION
Unless one were in a Rip Van Winkle-like state, it would be nearly impossible to be unaware of
the ethical and legal controversies surrounding the privacy-challenging practices of high-tech
organizations. The questionable business practices of Google, Facebook, Apple, and Amazon,
Received: 21 January 2020 Accepted: 23 February 2021
DOI: 10.1111/basr.12233
© 2021 W. Michael Hoffman Center for Business Ethics at Bentley University
Bus Soc Rev. 2021;126:155167. wileyonlinelibrary.com/journal/basr 155

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