Responsible AI: Gender bias assessment in emotion recognition

• URL: http://arxiv.org/abs/2103.11436v1
• Date: Sun, 21 Mar 2021 17:00:21 GMT
• Title: Responsible AI: Gender bias assessment in emotion recognition
• Authors: Artem Domnich and Gholamreza Anbarjafari
• Abstract summary: This research work aims to study a gender bias in deep learning methods for facial expression recognition. More biased neural networks show bigger accuracy gap in emotion recognition between male and female test sets.
• Score: 6.833826997240138
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Rapid development of artificial intelligence (AI) systems amplify many concerns in society. These AI algorithms inherit different biases from humans due to mysterious operational flow and because of that it is becoming adverse in usage. As a result, researchers have started to address the issue by investigating deeper in the direction towards Responsible and Explainable AI. Among variety of applications of AI, facial expression recognition might not be the most important one, yet is considered as a valuable part of human-AI interaction. Evolution of facial expression recognition from the feature based methods to deep learning drastically improve quality of such algorithms. This research work aims to study a gender bias in deep learning methods for facial expression recognition by investigating six distinct neural networks, training them, and further analysed on the presence of bias, according to the three definition of fairness. The main outcomes show which models are gender biased, which are not and how gender of subject affects its emotion recognition. More biased neural networks show bigger accuracy gap in emotion recognition between male and female test sets. Furthermore, this trend keeps for true positive and false positive rates. In addition, due to the nature of the research, we can observe which types of emotions are better classified for men and which for women. Since the topic of biases in facial expression recognition is not well studied, a spectrum of continuation of this research is truly extensive, and may comprise detail analysis of state-of-the-art methods, as well as targeting other biases.

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