Bridging the Gap: Protocol Towards Fair and Consistent Affect Analysis
This work addresses fairness issues in affect analysis for diverse subpopulations, providing a foundation for more equitable methodologies, though it is incremental as it builds on existing databases and methods.
The paper tackled biases in automatic affect analysis by analyzing six affective databases, annotating demographic attributes, and proposing a common protocol for fair database partitioning, revealing inadequacies in prior assessments through experiments with baseline and state-of-the-art methods.
The increasing integration of machine learning algorithms in daily life underscores the critical need for fairness and equity in their deployment. As these technologies play a pivotal role in decision-making, addressing biases across diverse subpopulation groups, including age, gender, and race, becomes paramount. Automatic affect analysis, at the intersection of physiology, psychology, and machine learning, has seen significant development. However, existing databases and methodologies lack uniformity, leading to biased evaluations. This work addresses these issues by analyzing six affective databases, annotating demographic attributes, and proposing a common protocol for database partitioning. Emphasis is placed on fairness in evaluations. Extensive experiments with baseline and state-of-the-art methods demonstrate the impact of these changes, revealing the inadequacy of prior assessments. The findings underscore the importance of considering demographic attributes in affect analysis research and provide a foundation for more equitable methodologies. Our annotations, code and pre-trained models are available at: https://github.com/dkollias/Fair-Consistent-Affect-Analysis