Difference between revisions of "Emotion Recognition Algorithms (ERAs)"

From SI410
Jump to: navigation, search
Line 5: Line 5:
 
==History==
 
==History==
  
Human emotions are crucial to understand emotion recognition technologies. Charles Darwin, in 2009, described the importance of human emotion in his book, ''The Expression of the Emotions in Man and Animals, Anniversary Edition''.<ref name="Darwin" /> Emotion recognition roots can be traced back to Paul Ekman<ref name="Ekman" /> and Wallace V. Friesen<ref name="Friesen"/> who, in 1975, identified what he found to be "basic" and "universal" emotions: anger, disgust, fear, joy, sadness, and surprise.<ref>Ekman, Paul and Friesen, Wallace V. (1975). ''Unmasking the face: A guide to recognizing emotions from facial cues.''</ref>. As technology has advanced, emotion recognition technologies such as emotion artificial intelligence algorithms have emerged using much of Ekman's work. These technologies are constantly being iteratively designed <ref name=”Fasel and Luettin"> Fasel, Beat and  Luettin, Juergen. (January, 2003). ''Automatic facial expression analysis: a survey.'' “Pattern Recognition 36.” Issue 1. https://doi.org/10.1016/S0031-3203(02)00052-3 </ref> Major companies are now using emotion recognition technologies to make decisions and curate content <ref name=”Google patent”>https://patents.google.com/patent/WO2017078960A1/en</ref> .  
+
Human emotions are crucial to understand emotion recognition technologies. Charles Darwin, in 2009, described the importance of human emotion in his book, ''The Expression of the Emotions in Man and Animals, Anniversary Edition''.<ref name="Darwin" /> Emotion recognition roots can be traced back to Paul Ekman<ref name="Ekman" /> and Wallace V. Friesen<ref name="Friesen"/> who, in 1975, identified what he found to be "basic" and "universal" emotions: anger, disgust, fear, joy, sadness, and surprise.<ref>Ekman, Paul and Friesen, Wallace V. (1975). ''Unmasking the face: A guide to recognizing emotions from facial cues.''</ref> As technology has advanced, emotion recognition technologies such as emotion artificial intelligence algorithms have emerged using much of Ekman's work. These technologies are constantly being iteratively designed. <ref name=”Fasel and Luettin"> Fasel, Beat and  Luettin, Juergen. (January, 2003). ''Automatic facial expression analysis: a survey.'' “Pattern Recognition 36.” Issue 1. https://doi.org/10.1016/S0031-3203(02)00052-3 </ref> Major companies are now using emotion recognition technologies to make decisions and curate content <ref name=”Google patent”>https://patents.google.com/patent/WO2017078960A1/en</ref> .  
  
 
==ERAs on Social Media==
 
==ERAs on Social Media==

Revision as of 01:33, 11 February 2022

Emotions.jpeg
Back • ↑Topics • ↑Categories

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 situations 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 literature on the importance of emotions such as Charles Darwin’s The Expression of the Emotions in Man and Animals, Anniversary Edition [3] and from Paul Ekman[4] and Wallace V. Friesen[5] 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

Human emotions are crucial to understand emotion recognition technologies. Charles Darwin, in 2009, described the importance of human emotion in his book, The Expression of the Emotions in Man and Animals, Anniversary Edition.[6] Emotion recognition roots can be traced back to Paul Ekman[4] and Wallace V. Friesen[7] who, in 1975, identified what he found to be "basic" and "universal" emotions: anger, disgust, fear, joy, sadness, and surprise.[8] As technology has advanced, emotion recognition technologies such as emotion artificial intelligence algorithms have emerged using much of Ekman's work. These technologies are constantly being iteratively designed. Cite error: Invalid <ref> tag; invalid names, e.g. too many Major companies are now using emotion recognition technologies to make decisions and curate content [9] .

ERAs on Social Media

Companies such as Google [10] have begun to use emotion recognition technologies with patents [11] 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 [12]. Emotion recognition technology can also help determine the state of wellbeing of users; for instance, emotion recognition algorithms on Facebook can be used to identify individuals in states of deep distress when posting about their emotions which can then curate helpful and supportive content to those individuals [13]. Emotion recognition on social media can, overall, enhance the user experience by optimizing the content and interactions exposed to the users using emotion recognition data [14]

Implications of ERAs

Benefits of ERAs

ERAs can be used in a plethora of ways. Emotion recognition can be used to provide social media users with curated content to enhance experiences on social mediaCite error: Invalid <ref> tag; invalid names, e.g. too many[14].

Harms of ERAs

Although emotion recognition technologies can help identify a potentially negative situation, ERAs come with harms as all other technologies. Emotion recognition often requires artificial intelligence bias mitigation methods such as auditing the data from artificial intelligence and highlighting disparities in data can influence companies to make changes [15]

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. Darwin, Charles. (2009). The Expression of the Emotions in Man and Animals, Anniversary Edition. (4 ed.). Oxford University Press.
  4. 4.0 4.1 https://en.wikipedia.org/wiki/Paul_Ekman
  5. https://en.wikipedia.org/wiki/Wallace_V._Friesen
  6. Cite error: Invalid <ref> tag; no text was provided for refs named Darwin
  7. Cite error: Invalid <ref> tag; no text was provided for refs named Friesen
  8. Ekman, Paul and Friesen, Wallace V. (1975). Unmasking the face: A guide to recognizing emotions from facial cues.
  9. https://patents.google.com/patent/WO2017078960A1/en
  10. Puneet Agrawal. 2017. Emotionally connected responses from a digital assistant. Retrieved June 26, 2019 from https://patents.google.com/patent/WO2017078960A1/en
  11. https://en.wikipedia.org/wiki/Patent
  12. https://en.wikipedia.org/wiki/Information_asymmetry
  13. https://engineering.fb.com/2018/02/21/ml-applications/under-the-hood-suicide-prevention-tools-powered-by-ai/
  14. 14.0 14.1 Wang, Yichen and Pal, Aditya. (2015). Detecting Emotions in Social Media: A Constrained Optimization Approach. In IJCAI.
  15. Raji, Inioluwa D. and Buolamwini, Joy. (2019). Actionable Auditing. Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (1 2019). https://doi.org/10.1145/3306618.3314244