Overview
Explore techniques for identifying and mitigating bias in AI systems with NLP expert Arnault Gombert.
Syllabus
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- Introduction to Bias in AI
-- Definition and types of bias in AI systems
-- Importance of addressing bias in AI
- Case Studies of Bias in AI Systems
-- Historical examples of bias in AI
-- Current high-profile cases and their impact
- Techniques for Identifying Bias in AI
-- Data analysis and pre-processing
-- Methods for bias detection in models and algorithms
-- Tools and frameworks for bias identification
- Bias in Natural Language Processing (NLP)
-- Specific biases in NLP models
-- Techniques for analyzing language model outputs
- Mitigation Strategies for Bias in AI
-- Data collection strategies to reduce bias
-- Algorithmic approaches and fairness constraints
-- Bias mitigation frameworks and tools
- Ethical Considerations and Regulations
-- Ethical frameworks for AI technology
-- Overview of legal and policy guidelines on AI bias
- Hands-On Workshops
-- Identifying bias in datasets using popular tools
-- Mitigating bias through model retraining and data augmentation
- Future Directions in Bias Mitigation
-- Advances in bias detection and reduction techniques
-- Potential future challenges in AI fairness
- Course Conclusion
-- Summarizing key takeaways
-- Discussion on continued learning and resources
Taught by
Tags
On-Demand
