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:
this https URL

机器学习算法在日常生活中的不断整合凸显了它们在部署中公平和公正的关键需要。情感分析的自动化，在生理学、心理学和机器学习的交叉点上，取得了显著的发展。然而，现有的数据库和方法缺乏统一性，导致存在偏见的评估。本研究通过对六个情感数据库进行分析，注释人口属性，并提出了一种常见的数据库分割协议来解决这些问题。强调在评估中的公平性。通过基准和最先进的方法进行了广泛的实验证明了这些变化的影响，揭示了先前评估的不足之处。该研究结果强调了在情感分析研究中考虑人口属性的重要性，并为更加公平的方法学提供了基础。我们的注释、代码和预训练模型可以在此 https URL 引用。