Explore the critical issue of bias in affect recognition algorithms through this insightful conference talk. Delve into an evaluation of data and algorithms used for facial affect recognition, focusing on potential biases related to gender, race, and age. Learn how researchers Jaspar Pahl, Ines Rieger, Anna Möller, Thomas Wittenberg, and Ute Schmid investigate the impact of these biases on the accuracy and fairness of emotion detection systems. Gain valuable insights into the challenges and implications of bias in AI-driven facial analysis, and understand the importance of developing more inclusive and equitable technologies in the field of affective computing.
Bias Evaluation on Data and Algorithms for Affect Recognition in Faces
Association for Computing Machinery (ACM) via YouTube
Overview
Syllabus
Female, white, 27? Bias Evaluation on Data and Algorithms for Affect Recognition in Faces
Taught by
ACM FAccT Conference
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