Human-First, Please

Assessing Citizen Views and Industrial Ambition for Emotional AI in Recommender Systems

Bibliographic Data

ID12729673
AuthorsVian Bakir (0000-0002-6828-8384, Bangor University, corresponding author), Alex Laffer (0000-0003-2463-9135, Bangor University), Andrew Mcstay (0000-0001-8928-3825, Bangor University)
Year2023
Volume21
Issue2
Pages205-222
Publication date2023-07-03
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueSurveillance & Society (JOURNAL)
Journal identifiersISSN: 1477-7487 • E-ISSN: 1477-7487
PublisherSurveillance Studies Network (PUBLISHER • GB)
DOI10.24908/ss.v21i2.16015
OpenAlexW4383106139
LanguageEN
Citations received4
References cited26

This paper qualitatively explores the views of diverse members of the British public on applications of biometric emotional AI technologies patented by two globally dominant consumer-facing recommender systems, Amazon and Spotify. Examining Amazon and Spotify patents for biometric profiling of users’ emotions, disposition, and behaviour to offer them tailored services, ads, and products from their wider platforms, this paper points to industrial ambition regarding emotional AI. Little is known about ordinary people’s views on deployment of such technology, and given the complex, abstract, and future-facing nature of such technologies, ascertaining informed lay views is hard. We address this through our innovative, qualitative study of diverse British-based adults (n=46) that presents to them near-horizon use cases in an interactive fictional narrative that deploys design fiction principles and ContraVision techniques. We find the themes of “usefulness,” “resignation,” “uneasy terms of engagement,” and “human-first,” adding rich and nuanced insights to prior survey work on users’ views towards biometric-based emotional AI technologies. In contributing to a richer understanding of whether emotional AI technologies should be deployed in consumer-facing recommender systems, and if so, on what terms, we find that well-established policy-friendly criticisms apply to global emotional AI recommender systems. We conclude, however, that problems of alienation and need for a human-first approach to emerging AI technology are the most significant criticisms

Alienation · Biometrics · Data science · Emerging technologies · Internet privacy · Narrative · Political science · Profiling (computer programming · Public engagement · Public relations · Recommender system · Sociology · Software deployment · World Wide Web · Computer Science · Digital Mental Health Interventions · Ethics and Social Impacts of AI · Law · Mental Health via Writing · Artificial Intelligence

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