Ethical AI Frameworks & Bias Mitigation
Developing robust frameworks for ethical AI with bias mitigation techniques - November 2024 Edition.
Authors:
Dr. Leonardo Torres, Dr. Michael Carter, Dr. Hana Kim
Journal Reference:
AI Ethics Journal, Vol. 12 (2024)
Presented at the 2024 Conference
Abstract
This research introduces a comprehensive ethical AI framework that integrates transparent decision-making with bias mitigation techniques. By combining algorithmic auditing processes with explainable AI (XAI) methods, we achieve a 75% improvement in accountability while reducing discriminatory outcomes in machine learning systems.
Through extensive testing on 12 benchmark datasets, our framework detects and neutralizes hidden biases with 89% accuracy. The implementation supports real-time fairness monitoring and includes compliance tools for regulatory adherence across GDPR, CCPA, and emerging AI laws.
Key Innovations
Bias Detection Framework
Integrated bias metrics across all training stages with continuous fairness monitoring for machine learning models.
Explainability Tools
Real-time visualization of decision-making processes with clear audit trails for all AI-based conclusions.
Regulatory Compliance
Automated policy checkers ensure alignment with GDPR, HIPAA, and AI Act requirements for enterprise AI deployments.
Originally Presented at NeuroNexus 2024
This ethical AI research was presented at the 2024 NeuroNexus Symposium on October 15. See our full conference archive for more details.