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AI fairness
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- Article · AI Agents
Validating Explainability in EduTech Credit Models: A Multi-Agent RAG Framework with SHAP/LIME Verification, MCP Integration
Learn how to build a domain-specific explainability validation framework for EduTech credit models using SHAP, LIME, LangGraph, Chroma, persistent memory, and Model Context Protocol (MCP). This article explores how to validate AI-generated explanations across technical soundness, pedagogical validity, regulatory compliance, and historical consistency. It demonstrates a multi-agent architecture that combines learning-science knowledge, live student performance data, policy guidance, and explanation history to identify unstable or misleading explanations before they reach students, advisors, or compliance teams.
- Article · AI
What are the Main Ethical Concerns around AI? (Bias, Privacy, Fairness)
This article explores the main ethical concerns surrounding artificial intelligence (AI), including bias, privacy violations, lack of transparency, and accountability issues. We’ll also discuss the real-world implications of these risks and propose solutions to ensure AI remains fair, safe, and beneficial for society.
