Emotion Recognition Algorithms (ERAs)

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Emotion recognition algorithms (ERAs) or emotion artificial intelligence (EAIs) are a form of technology that recognizes, infers, and harvests "emotions using data sources such as social media behavior, streaming service use, voice, facial expressions, and biometrics" [1]. ERAs are artificial intelligence technologies that are used in many different contexts such as employment [2], and these algorithms can be useful in identifying information pertaining to different contexts, but they may also be invasive to individuals who feel uncomfortable being closely observed [1]. The history of emotion recognition comes from Paul Ekman [3] who identified different but salient human emotions which became used throughout emotion recognition technologies. As technology has advanced overtime, ERAs have been implemented into everyday life including social media. Discussions have emerged on the benefits and harms of emotion recognition technologies and the implications it can have on technology users.

History

Emotion recognition roots can be traced back to Paul Ekman [3] who, in 1975, identified what he found to be "basic" and "universal" emotions: anger, disgust, fear, joy, sadness, and surprise [4]. As technology has advanced, emotion recognition technologies such as emotion artificial intelligence algorithms have emerged using much of Ekman's work. Major companies are now using emotion recognition technologies to make decisions and curate content.

ERAs on Social Media

Companies such as Google [5] have begun to use emotion recognition technologies with patents [6] to analyze user data through emotions and to then curate content for users. Companies with emotion recognition data that are gathered from their users have control over how the data is used which can be referred to as information asymmetry [7].

Implications of ERAs

Benefits of ERAs

Harms of ERAs

See also

References

  1. 1.0 1.1 Andalibi, Nazanin and Buss, Justin. (2020). The Human in Emotion Recognition on Social Media: Attitudes, Outcomes, Risks;'. CHI 2020.
  2. Robert, L. P., Pierce, C., Marquis, L., Kim, S., & Alahmad, R. (2020). Designing fair AI for managing employees in organizations: a review, critique, and design agenda. Human–Computer Interaction, 35(5-6), 545-575.
  3. 3.0 3.1 https://en.wikipedia.org/wiki/Paul_Ekman
  4. Paul Ekman and Wallace V Friesen. 1975. Unmasking the face: A guide to recognizing emotions from facial cues.
  5. https://patents.google.com/patent/WO2017078960A1/en
  6. https://en.wikipedia.org/wiki/Patent
  7. https://en.wikipedia.org/wiki/Information_asymmetry