Safety and Bias Mitigation in Fine-Tuned Models Training Course
As artificial intelligence becomes increasingly integrated into decision-making processes across various industries, and as regulatory standards continue to evolve, the importance of safety and bias mitigation in fine-tuned models has grown significantly.
This instructor-led live training, available both online and onsite, is designed for intermediate-level machine learning engineers and AI compliance professionals. The course aims to equip participants with the skills to identify, evaluate, and reduce safety risks and biases within fine-tuned language models.
Upon completion of this training, participants will be able to:
- Grasp the ethical and regulatory frameworks underpinning safe AI systems.
- Recognize and assess common types of bias found in fine-tuned models.
- Implement bias mitigation strategies during the training process and in post-processing stages.
- Develop and audit models to ensure safety, transparency, and fairness.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical applications.
- Hands-on implementation within a live laboratory environment.
Customization Options for the Course
- To arrange customized training for this course, please contact us.
Course Outline
Foundations of Safe and Fair AI
- Core concepts: safety, bias, fairness, and transparency.
- Types of bias: dataset, representation, and algorithmic.
- Overview of regulatory frameworks (e.g., EU AI Act, GDPR).
Bias in Fine-Tuned Models
- How fine-tuning can introduce or amplify bias.
- Case studies and real-world failures.
- Identifying bias in datasets and model predictions.
Techniques for Bias Mitigation
- Data-level strategies (rebalancing, augmentation).
- In-training strategies (regularization, adversarial debiasing).
- Post-processing strategies (output filtering, calibration).
Model Safety and Robustness
- Detecting unsafe or harmful outputs.
- Handling adversarial inputs.
- Red teaming and stress testing fine-tuned models.
Auditing and Monitoring AI Systems
- Bias and fairness evaluation metrics (e.g., demographic parity).
- Explainability tools and transparency frameworks.
- Ongoing monitoring and governance practices.
Toolkits and Hands-On Practice
- Using open-source libraries (e.g., Fairlearn, Transformers, CheckList).
- Hands-on: Detecting and mitigating bias in a fine-tuned model.
- Generating safe outputs through prompt design and constraints.
Enterprise Use Cases and Compliance Readiness
- Best practices for integrating safety in LLM workflows.
- Documentation and model cards for compliance.
- Preparing for audits and external reviews.
Summary and Next Steps
Requirements
- A solid understanding of machine learning models and their training methodologies.
- Practical experience with fine-tuning and Large Language Models (LLMs).
- Familiarity with Python programming and Natural Language Processing (NLP) concepts.
Target Audience
- AI compliance teams.
- Machine learning engineers.
Open Training Courses require 5+ participants.
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