Explainable Speech Emotion Recognition: Weighted Attribute Fairness to Model Demographic Contributions to Social Bias
Speech Emotion Recognition (SER) systems have growing applications in sensitive domains such as mental health and education, where biased predictions can cause harm. Traditional fairness metrics, such as Equalised Odds and Demographic Parity, often overlook the joint dependency between demographic attributes and model predictions. We propose a fairness modelling approach for SER that explicitly captures allocative bias by learning the joint relationship between demographic attributes and model e
Record details
Published: 26 March 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Healthcare · Children & education · Transparency
Retrieved: 14 July 2026
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How to cite this record
ethics.ai (26 March 2026), “Explainable Speech Emotion Recognition: Weighted Attribute Fairness to Model Demographic Contributions to Social Bias,” evidence record 6689, https://ethics.ai/record/6689 (originally published by arXiv).
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