In this episode of Ethical AI, we move from theory to practice. You’ll see exactly how to build ethical AI models with fairness in mind – starting from data sourcing to algorithm design and ongoing monitoring. We’ll work through a real-world scenario: building a resume screening AI that avoids bias and ensures fair opportunities for candidates across gender, race, and other protected groups. 🔑 What you’ll learn in this episode: 00:00 – Intro & Recap 00:06 – Building Ethical Models: Data & Design 00:34 - BUILDING A HIRING AI 02:40 – STEP 1: ETHICAL DATA SOURCING 03:57 – STEP 2: FEATURE SELECTION & PROXY VARIABLES 04:48 – STEP 3: ALGORITHMIC DESIGN CHOICES 05:45 – STEP 4: BUILT-IN MONITORING & AUDITING 06:15 – TESTING & VALIDATION 📂 Resources & Code: 👉Comment "Code" 💡 Next episode: We’ll cover the human side – diverse teams, stakeholder input, and organizational challenges in ethical AI. If you found this useful, like 👍, subscribe 🔔, and share 💬. Let me know in the comments: 👉 What challenges did you face when trying to build fair AI systems? #EthicalAI #ResponsibleAI #FairnessInAI #AIethics #MachineLearning #AIforGood #BiasInAI #DataScience #ArtificialIntelligence #TechForGood #AIethicsInPractice