EquiVerify: A Systematic Framework for Bias Mitigation and Reliable AI-Based Digital Identity Verification
Keywords:
Artificial Intelligence, Bias Reduction, Biometric Authentication, Fairness-Aware Learning, Federated Learning, Identity Verification, Reliability AnalysisAbstract
AI-driven digital identity verification increasingly governs high-stakes digital interactions, yet systemic model biases and demographic disparities can undermine reliability, fairness, and operational trust. This research introduces EquiVerify, a comprehensive framework for assessing, quantifying, and mitigating algorithmic bias in multi-modal identity verification systems. The approach combines demographic-aware performance metrics, counterfactual fairness analysis, and Bayesian reliability estimation to detect latent disparities across populations in biometric, behavioral, and relational datasets. To reduce unfair outcomes, the framework integrates fairness-constrained optimization and adversarial re-weighting during model training, ensuring equitable representation of underrepresented and minority identity patterns without compromising overall detection accuracy. Furthermore, EquiVerify incorporates robustness evaluation under distributional shifts and adversarial perturbations, ensuring sustained performance across evolving operational conditions. Extensive experiments on multi-ethnic, cross-institutional datasets demonstrate that EquiVerify reduces demographic performance gaps by up to 20–25% while maintaining state-of-the-art verification accuracy. The study highlights that proactive bias detection, mitigation, and reliability analysis are critical for the deployment of trustworthy and equitable AI systems in large-scale digital ecosystems. These findings establish a technical and operational methodology for institutions seeking to implement fair, transparent, and resilient identity verification pipelines across diverse populations.
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