Safety and Bias Mitigation in Fine-Tuned Models Training Course
As artificial intelligence becomes increasingly integral to decision-making processes across various sectors and regulatory frameworks continue to evolve, ensuring safety and mitigating bias in fine-tuned models has become a critical priority.
This instructor-led live training, available either online or onsite, is designed for mid-level machine learning engineers and AI compliance specialists who aim to identify, assess, and reduce safety risks and biases associated with fine-tuned language models.
Upon completing this training, participants will be equipped to:
- Grasp the ethical and regulatory landscape surrounding safe AI systems.
- Recognize and evaluate prevalent forms of bias within fine-tuned models.
- Implement bias mitigation strategies during and after the training phase.
- Develop and audit models with a focus on safety, transparency, and fairness.
Course Format
- Interactive lectures and group discussions.
- Extensive exercises and practical practice sessions.
- Hands-on implementation within a live laboratory environment.
Customization Options
- For requests regarding customized training for this course, please contact us to make arrangements.
Course Outline
Foundations of Safe and Fair AI
- Core concepts: safety, bias, fairness, and transparency
- Categories of bias: dataset, representation, and algorithmic
- Overview of regulatory frameworks, including the EU AI Act and GDPR
Bias in Fine-Tuned Models
- Understanding how fine-tuning can introduce or amplify bias
- Analysis of case studies and real-world failures
- Techniques for identifying bias in datasets and model predictions
Techniques for Bias Mitigation
- Data-level strategies such as rebalancing and augmentation
- In-training strategies including regularization and adversarial debiasing
- Post-processing strategies like output filtering and calibration
Model Safety and Robustness
- Detecting unsafe or harmful outputs
- Handling adversarial inputs
- Conducting red teaming and stress testing on fine-tuned models
Auditing and Monitoring AI Systems
- Evaluating bias and fairness metrics, such as demographic parity
- Utilizing explainability tools and transparency frameworks
- Establishing ongoing monitoring and governance practices
Toolkits and Hands-On Practice
- Leveraging open-source libraries such as Fairlearn, Transformers, and CheckList
- Practical session: Detecting and mitigating bias in a fine-tuned model
- Generating safe outputs through effective prompt design and constraints
Enterprise Use Cases and Compliance Readiness
- Best practices for integrating safety measures into LLM workflows
- Documentation standards and model cards for compliance
- Preparing for audits and external reviews
Summary and Next Steps
Requirements
- Foundational knowledge of machine learning models and training methodologies
- Practical experience with fine-tuning techniques and Large Language Models (LLMs)
- Proficiency in Python and familiarity with Natural Language Processing (NLP) concepts
Target Audience
- AI compliance teams
- Machine Learning engineers
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