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Title: The Human in Emotion Recognition on Social Media: Attitudes, Outcomes, Risks
Emotion recognition algorithms recognize, infer, and harvest emotions using data sources such as social media behavior, streaming service use, voice, facial expressions, and biometrics in ways often opaque to the people providing these data. People's attitudes towards emotion recognition and the harms and outcomes they associate with it are important yet unknown. Focusing on social media, we interviewed 13 adult U.S. social media users to fill this gap. We find that people view emotions as insights to behavior, prone to manipulation, intimate, vulnerable, and complex. Many find emotion recognition invasive and scary, associating it with autonomy and control loss. We identify two categories of emotion recognition's risks: individual and societal. We discuss findings' implications for algorithmic accountability and argue for considering emotion data as sensitive. Using a Science and Technology Studies lens, we advocate that technology users should be considered as a relevant social group in emotion recognition advancements.  more » « less
Award ID(s):
2020872
NSF-PAR ID:
10437787
Author(s) / Creator(s):
;
Date Published:
Journal Name:
CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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