Measuring Automated Influence: Between Empirical Evidence and Ethical Values

Daniel Susser, Vincent Grimaldi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Scopus citations

Abstract

Automated influence, delivered by digital targeting technologies such as targeted advertising, digital nudges, and recommender systems, has attracted significant interest from both empirical researchers, on one hand, and critical scholars and policymakers on the other. In this paper, we argue for closer integration of these efforts. Critical scholars and policymakers, who focus primarily on the social, ethical, and political effects of these technologies, need empirical evidence to substantiate and motivate their concerns. However, existing empirical research investigating the effectiveness of these technologies (or lack thereof), neglects other morally relevant effects-which can be felt regardless of whether or not the technologies "work"in the sense of fulfilling the promises of their designers. Drawing from the ethics and policy literature, we enumerate a range of questions begging for empirical analysis-the outline of a research agenda bridging these fields - -and issue a call to action for more empirical research that takes these urgent ethics and policy questions as their starting point.

Original languageEnglish (US)
Title of host publicationAIES 2021 - Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society
PublisherAssociation for Computing Machinery, Inc
Pages242-253
Number of pages12
ISBN (Electronic)9781450384735
DOIs
StatePublished - Jul 21 2021
Event4th AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society, AIES 2021 - Virtual, Online, United States
Duration: May 19 2021May 21 2021

Publication series

NameAIES 2021 - Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society

Conference

Conference4th AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society, AIES 2021
Country/TerritoryUnited States
CityVirtual, Online
Period5/19/215/21/21

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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