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Ethical and Explainable AI in Reusable MLOps Pipelines
arXiv – CS AI|Rakib Hossain, Mahmood Menon Khan, Lisan Al Amin, Dhruv Parikh, Farhana Afroz, Bestoun S. Ahmed|
🤖AI Summary
Researchers developed a unified MLOps framework that integrates ethical AI principles, reducing demographic bias from 0.31 to 0.04 while maintaining predictive accuracy. The system automatically blocks deployments and triggers retraining based on fairness metrics, demonstrating practical implementation of ethical AI in production environments.
Key Takeaways
- →Framework reduces demographic parity difference from 0.31 to 0.04 without model retuning while achieving 0.89 AUC on validation datasets.
- →System automatically blocks model deployment if demographic parity difference or equalized odds exceed 0.05 threshold.
- →Production deployments consistently maintained fairness metrics with DPD ≤ 0.05 and EO ≤ 0.03.
- →Automated retraining triggers when 30-day Kolmogorov-Smirnov drift statistic exceeds 0.20.
- →Framework demonstrates that ethical AI constraints can be implemented without disrupting operational workflows.
#ethical-ai#mlops#bias-reduction#fairness-metrics#automated-deployment#ai-governance#machine-learning#production-ai
Read Original →via arXiv – CS AI
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