Responsible AI: Gender bias assessment in emotion recognition

Artem Domnich · Gholamreza Anbarjafari

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Ethics and Social Impacts of AI Emotion and Mood Recognition Face recognition and analysis

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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References

1980A circumplex model of affect.2021A Survey on Bias and Fairness in Machine Learning 2016Equality of Opportunity in Supervised Learning 2017AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild 2016The social dilemma of autonomous vehicles 2017Reliable Crowdsourcing and Deep Locality-Preserving Learning for Expression Recognition in the Wild 2017Counterfactual Fairness 2016Training deep networks for facial expression recognition with crowd-sourced label distribution

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2025Domain adaptation for bias mitigation in affective computing: use cases for facial emotion recognition and sentiment analysis systems 2023Beyond Data 2021Assessing Gender Bias in Predictive Algorithms using eXplainable AI 2024U.S. Job-Seekers' Organizational Justice Perceptions of Emotion AI-Enabled Interviews 2022Mapping the Multilingual Margins: Intersectional Biases of Sentiment Analysis Systems in English, Spanish, and Arabic

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