Auditing Facial Emotion Recognition Datasets for Posed Expressions and Racial Bias
2025-07-16
Summary
The study audits two facial emotion recognition (FER) datasets, AffectNet and RAF-DB, for biases related to posed expressions and racial disparities. It finds a significant number of posed images, which can skew model performance in real-world applications. Additionally, models trained on these datasets exhibit racial bias, often misclassifying emotions for individuals with darker skin tones or those perceived as non-white.
Why This Matters
Facial emotion recognition technology is increasingly used in applications like security and human-computer interaction. The identified biases could lead to harmful outcomes, such as misinterpretations of emotion based on race, thereby reinforcing social stereotypes. Understanding and addressing these biases is crucial for developing fair and effective AI systems.
How You Can Use This Info
Professionals in AI and technology fields can use these findings to reassess the datasets and models they use, ensuring they are aware of potential biases. Organizations employing FER technologies should be cautious of these issues and consider adopting more inclusive and representative data collection practices. This awareness can guide ethical AI deployment and help mitigate risks associated with biased AI applications.