Safety and Bias Mitigation in Fine-Tuned Models Training Course
The issue of safety and bias mitigation in fine-tuned models is becoming increasingly critical as artificial intelligence becomes more integrated into decision-making processes across various industries, while regulatory standards continue to evolve.
This instructor-led, live training session (available online or on-site) is designed for ML engineers and AI compliance professionals at an intermediate level who aim to identify, evaluate, and mitigate safety risks and biases in fine-tuned language models.
Upon completion of this training, participants will be capable of:
- Grasping the ethical and regulatory framework governing safe AI systems.
- Identifying and assessing common types of bias present in fine-tuned models.
- Implementing bias mitigation techniques both during and after the training phase.
- Designing and auditing models to ensure safety, transparency, and fairness.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical activities.
- Hands-on implementation within a live laboratory environment.
Customization Options
- To request tailored 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
- Types of bias: dataset bias, representation bias, and algorithmic bias
- Overview of relevant regulatory frameworks (e.g., EU AI Act, GDPR)
Bias in Fine-Tuned Models
- Understanding how fine-tuning can introduce or exacerbate bias
- Analysis of case studies and real-world failures
- Strategies for identifying bias in datasets and model predictions
Techniques for Bias Mitigation
- Data-level strategies (data rebalancing, augmentation)
- In-training strategies (regularization, adversarial debiasing)
- Post-processing strategies (output filtering, calibration)
Model Safety and Robustness
- Detecting unsafe or harmful model outputs
- Handling adversarial inputs
- Conducting red teaming and stress testing on fine-tuned models
Auditing and Monitoring AI Systems
- Evaluating bias and fairness metrics (e.g., demographic parity)
- Utilizing explainability tools and transparency frameworks
- Implementing ongoing monitoring and governance practices
Toolkits and Hands-On Practice
- Leveraging open-source libraries (e.g., Fairlearn, Transformers, CheckList)
- Practical session: 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 into LLM workflows
- Documentation and model cards for compliance purposes
- Preparing for audits and external reviews
Summary and Next Steps
Requirements
- Fundamental knowledge of machine learning models and training methodologies
- Practical experience with fine-tuning techniques and Large Language Models (LLMs)
- Familiarity with Python programming and Natural Language Processing (NLP) concepts
Target Audience
- AI compliance teams
- ML engineers
Open Training Courses require 5+ participants.
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