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The growth of technologies promising to infer emotions raises political and ethical concerns, including concerns regarding their accuracy and transparency. A marginalized perspective in these conversations is that of data subjects potentially affected by emotion recognition. Taking social media as one emotion recognition deployment context, we conducted interviews with data subjects (i.e., social media users) to investigate their notions about accuracy and transparency in emotion recognition and interrogate stated attitudes towards these notions and related folk theories. We find that data subjects see accurate inferences as uncomfortable and as threatening their agency, pointing to privacy and ambiguity as desired design principles for social media platforms. While some participants argued that contemporary emotion recognition must be accurate, others raised concerns about possibilities for contesting the technology and called for better transparency. Furthermore, some challenged the technology altogether, highlighting that emotions are complex, relational, performative, and situated. In interpreting our findings, we identify new folk theories about accuracy and meaningful transparency in emotion recognition. Overall, our analysis shows an unsatisfactory status quo for data subjects that is shaped by power imbalances and a lack of reflexivity and democratic deliberation within platform governance.
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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.
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- Award ID(s):
- 2020872
- PAR ID:
- 10437787
- 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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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3313831.3376680
