A CONCEPTUAL FRAMEWORK FOR AI SELF-HEALING FOR BIAS MITIGATION: A PROACTIVE ARCHITECTURAL PROPOSAL

Abstract

As the adoption of Artificial Intelligence (AI) continues to expand across various sectors, the issue of bias in training data has emerged as a significant ethical and technical challenge. AI systems are commonly trained using large-scale datasets collected from digital environments such as the internet, social media, and public databases. These datasets often contain historical inequalities, stereotypes, and unbalanced representations of certain demographic groups. Consequently, AI models may unintentionally replicate and amplify these biases in their predictions or decisions. This situation becomes particularly concerning when AI is used in high-stakes domains such as recruitment, healthcare, financial services, and public policy. Most existing bias mitigation strategies rely on reactive approaches, such as adjusting model outputs or modifying datasets after bias has already been identified. While these methods can reduce certain forms of discrimination, they often require significant manual intervention and may not effectively address bias in dynamic data environments. This research proposes a conceptual framework for an AI self-healing system designed to autonomously detect and correct bias in training data before it influences model outcomes. The proposed framework integrates four key modules: Data Monitoring, Bias Analysis, Automated Bias Correction, and a Feedback Loop and Validation mechanism. Together, these components create a continuous workflow that allows the system to identify bias patterns, apply corrective strategies, and verify fairness before data is used for model training. This framework offers a proactive and sustainable approach to bias mitigation while supporting the development of more ethical, robust, and accountable AI systems.

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