Ethical AI Episode 3: Building Ethical Models with Fair Data & Design

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

Видео Ethical AI Episode 3: Building Ethical Models with Fair Data & Design канала TrashToTeach