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Starts 4 June 2025 06:44
Ends 4 June 2025
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Preventing Toxicity and Unconscious Biases Using Large Language and Deep Learning Models
Discover how large language models and BERT transformers can detect and prevent unconscious biases in AI systems, achieving 98.7% accuracy across diverse data sources and cultural contexts.
OpenInfra Foundation
via YouTube
OpenInfra Foundation
2416 Courses
40 minutes
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Overview
Discover how large language models and BERT transformers can detect and prevent unconscious biases in AI systems, achieving 98.7% accuracy across diverse data sources and cultural contexts.
Syllabus
- Introduction to AI Bias and Toxicity
- Large Language Models: Fundamentals
- Detecting Biases with AI
- Techniques for Mitigating AI Biases
- Large Language Models in Practice
- Evaluating and Measuring Model Performance
- Case Studies
- Ethical Considerations and Best Practices
- Practical Workshop
- Conclusion and Future Directions
Overview of biases in AI systems
Impact of toxicity in AI-generated content
Structure and function of large language models
Overview of BERT and transformers
Techniques for identifying bias
Evaluating model performance in bias detection
Algorithmic fairness
Data preprocessing and augmentation strategies
Training BERT for bias detection
Fine-tuning models for specific cultural contexts
Accuracy, precision, and recall metrics
Achieving and measuring 98.7% accuracy
Real-world applications and their challenges
Analysis of successful bias mitigation
Developing ethical AI systems
Guidelines for fairness and transparency
Hands-on training with BERT-based models
Bias detection and mitigation exercises
Emerging trends and technologies in AI fairness
Future opportunities for research and development
Subjects
Data Science
