Discover how to build safe, fair, and reliable AI models in this talk by David Talby, PhD. In "Applying Responsible AI with Open-Source Tools," you'll learn practical techniques to tackle four common challenges in AI development: robustness, labeling errors, bias, and data leakage. Gain hands-on knowledge of open-source tools and real-world examples to enhance your machine learning, NLP, and data science projects. Perfect for data science practitioners and leaders, this session equips you with actionable strategies to ensure your AI systems perform safely and correctly in the real world.
Explore the tools that help detect and fix labeling errors, test model robustness, and address bias across various critical groups. Understand how to prevent data leakage, especially when dealing with personally identifiable information.
#AI #MachineLearning #DataScience #NLP #DeepLearning #ArtificialIntelligence #DataEngineering #ResponsibleAI #DataVisualization #TechTalk #OpenSource #AITools #MLTraining #ODSC
Timecodes:
0:00 - Intro
0:33 - Current Gaps in Responsible AI
8:04 - Robustness, Exploratory, and Bias Training
26:33 - The NLP Test Library
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