⚖️ Ethical AI: Bias Mitigation and Trust Architecture

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Analysis of ethical AI, moving beyond theoretical discussions to explore its practical implementation and governance. It outlines core principles for trustworthy AI, such as fairness, transparency, and accountability, emphasizing that ethical considerations must be integrated throughout the AI lifecycle, not merely as an afterthought. The document categorizes various sources and types of algorithmic bias, from data collection to deployment, providing real-world examples in high-stakes domains like healthcare and criminal justice. Furthermore, it details technical mitigation strategies—pre-processing, in-processing, and post-processing—and discusses the importance of Explainable AI (XAI), Model Cards, and continuous validation in building and maintaining trust. Finally, the text examines regulatory frameworks (e.g., NIST AI RMF, EU AI Act), professional codes, and corporate responsible AI practices, highlighting the emergence of a shared "accountability supply chain" and the reframing of ethics through a risk management lens.