Bias Identification and Mitigation in AI Systems

Overview

Explore techniques for identifying and mitigating bias in AI systems with NLP expert Arnault Gombert.

Syllabus

• 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

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Subjects

Data Science