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Applying Responsible AI with Open-Source Tools

Offered By: Open Data Science via YouTube

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Responsible AI Courses Data Science Courses Machine Learning Courses AI Ethics Courses

Course Description

Overview

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Learn practical techniques for building safe, fair, and reliable AI models in this 42-minute talk by David Talby, PhD. Address four common challenges in AI development: robustness, labeling errors, bias, and data leakage using open-source tools and real-world examples. Gain actionable strategies to enhance machine learning, NLP, and data science projects, ensuring AI systems perform safely and correctly in real-world scenarios. Explore tools for detecting and fixing labeling errors, testing model robustness, and addressing bias across critical groups. Discover methods to prevent data leakage, particularly when handling personally identifiable information. Perfect for data science practitioners and leaders looking to implement responsible AI practices in their work.

Syllabus

- Intro
- Current Gaps in Responsible AI
- Robustness, Exploratory, and Bias Training
- The NLP Test Library


Taught by

Open Data Science

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