Safety and Bias Mitigation in Fine-Tuned Models Training Course
As artificial intelligence becomes increasingly integral to decision-making processes across various industries and regulatory frameworks continue to evolve, the importance of safety and bias mitigation in fine-tuned models has grown significantly.
This instructor-led, live training, available either online or onsite, is designed for intermediate-level machine learning engineers and AI compliance professionals who seek to identify, evaluate, and reduce safety risks and biases within fine-tuned language models.
Upon completing this training, participants will be able to:
- Comprehend the ethical and regulatory landscape governing safe AI systems.
- Identify and assess common types of bias present in fine-tuned models.
- Implement bias mitigation strategies during and after the training phase.
- Design and audit models with a focus on safety, transparency, and fairness.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical practice.
- Hands-on implementation in a live laboratory environment.
Course Customization Options
- To request customized training for this course, please contact us to make arrangements.
Course Outline
Foundations of Safe and Fair AI
- Key concepts: safety, bias, fairness, transparency
- Types of bias: dataset, representation, algorithmic
- Overview of regulatory frameworks (EU AI Act, GDPR, etc.)
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
- Adversarial input handling
- 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
- Understanding of machine learning models and training processes
- Experience with fine-tuning and Large Language Models (LLMs)
- Familiarity with Python and Natural Language Processing (NLP) concepts
Audience
- AI compliance teams
- Machine learning engineers
Open Training Courses require 5+ participants.
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