🧠 Building an Expectation-Based AI Governance Model (EBAGM) in Python

🧠 Building an Expectation-Based AI Governance Model (EBAGM) in Python What if AI governance wasn’t just about accuracy—but about aligning with human expectations? 🚀 Introduction Most AI systems today are evaluated using metrics like accuracy, precision, and recall . But in real-world scenarios, that’s not enough. A model can be technically correct and still feel unfair, biased, or unethical to humans. This is where a new idea comes in: Expectation-Based AI Governance Model (EBAGM) Instead of only focusing on data and outputs, EBAGM introduces: Human expectations Perceived intent Ethical alignment In this blog, I’ll walk you through building a working prototype in Python . ⚙️ What is EBAGM? EBAGM is a governance framework with 5 layers: Expectation Layer (E) → What humans expect (fairness, privacy, etc.) Data Governance (D → D') → Modify data based on expectations Model (M) → AI decision-making Perceived Intent (P) → Does AI feel fair? Feedback Loop → Adjust system if misaligned 🧪 Step