Faces of Fairness: Examining Bias in Facial Expression Recognition Datasets and Models

A recent study delves into the bias and fairness issues present in Facial Expression Recognition (FER) datasets and models, highlighting the importance of these factors in AI system development. By analyzing four widely used FER datasets—AffectNet, ExpW, Fer2013, and RAF-DB—the research uncovers various sources of bias that could impact the effectiveness and fairness of AI applications. This investigation is crucial as it sheds light on the often-overlooked aspects of AI ethics, ensuring that future developments in facial recognition technology are more equitable and reliable.