Safety and Bias Mitigation in Fine-Tuned Models Training Course
Ensuring safety and mitigating bias in fine-tuned models is an increasingly critical issue as artificial intelligence becomes deeply integrated into decision-making processes across various industries and as regulatory standards continue to evolve.
This instructor-led live training, available either online or onsite, targets intermediate-level machine learning engineers and AI compliance professionals who aim to identify, evaluate, and reduce safety risks and biases in fine-tuned language models.
Upon completing this training, participants will be able to:
- Grasp the ethical and regulatory context required for safe AI systems.
- Identify and assess common forms of bias present in fine-tuned models.
- Implement bias mitigation techniques 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 activities.
- Hands-on implementation within 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
- A foundational understanding of machine learning models and training processes.
- Experience working with fine-tuning techniques and Large Language Models (LLMs).
- Familiarity with Python programming and Natural Language Processing (NLP) concepts.
Audience
- AI compliance teams.
- Machine learning engineers.
Open Training Courses require 5+ participants.
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