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!
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GitHub Repo: github.com/AIA...
Intro Video: • Learn to Evaluate LLMs...
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[@tlong900]

Thank you for this series. Really appreciate it

[@AIAnytime]

Glad you enjoy it!

[@onionisnoopinionbutagift2987]

Very nice videos and very helpful - thank you a lot. Do you have by any chance a reference to the paper that introduce demographic diversity analysis? I tried to find it online, but I failed so far.

[@sanjayojha1]

I like the series, but we need more explanation to the certain part of codes, for example code starting from 9:10

[@Jeganbaskaran]

How the evaluation metics can be used in the real world scenorio for the huge dataset. Do we need to have any intermediate layer before we respond to the users?

[@ashisranjanlahiri]

Good work

[@AIAnytime]

Thanks

[@giridharreddy7011]

More videos on RAG evaluation

[@vijaybudhewar7014]

This Demographic evaluation should fall under LLM assisted section right? arent we using LLM's response for this ?

[@AIAnytime]

This is correct in this context.