  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
