Evaluating Biases in LLMs using WEAT and Demographic Diversity Analysis скачать в хорошем качестве

Evaluating Biases in LLMs using WEAT and Demographic Diversity Analysis

In today's tutorial, I dive deep into the world of Responsible AI, shedding light on how to evaluate biases in Large Language Models (LLMs) using the Word Embedding Association Test (WEAT) and Demographic Diversity Analysis. Understand the mathematical intuition, and real-world implications, and get hands-on with Python code examples to gauge the performance of these models across different demographic groups. Bias in AI models can lead to unfair outcomes, and it's crucial for us to identify and mitigate them. Join me in this journey to ensure our AI systems are fair, inclusive, and responsible. 🔍 Topics Covered: Introduction to WEAT Mathematical intuition behind WEAT Demographic Diversity Analysis in LLMs Practical Python code demonstrations Interpretation of results and recommendations 👍 If you found this tutorial insightful, please give it a thumbs up—it helps a lot! 💬 Have questions or insights? Drop a comment below; I'd love to hear from you! 🔔 And don't forget to subscribe for more content on Generative AI. GitHub Repo: https://github.com/AIAnytime/Evaluati... Intro Video:    • Learn to Evaluate LLMs and RAG Approaches   #generativeai #ai #genai