Safety and Bias Mitigation in Fine-Tuned Models Training Course
As artificial intelligence becomes increasingly integral to decision-making 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 either online or onsite, is designed for intermediate-level machine learning engineers and AI compliance professionals who want to learn how to identify, assess, and minimize safety risks and biases in fine-tuned language models.
Upon completion of this training, participants will be able to:
- Grasp the ethical and regulatory landscape governing safe AI systems.
- Recognize and evaluate common types of bias found in fine-tuned models.
- Implement bias mitigation strategies both during and after the training process.
- Develop and audit models with a focus on safety, transparency, and fairness.
Course Format
- Interactive lectures and group discussions.
- Extensive exercises and practical sessions.
- Hands-on implementation within a live laboratory environment.
Customization Options
- To arrange a customized training session for this course, please contact us directly.
Course Outline
Foundations of Safe and Fair AI
- Core concepts: safety, bias, fairness, and transparency.
- Types of bias: dataset, representation, and algorithmic biases.
- 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.
- Case studies and real-world failure examples.
- Methods 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, such as output filtering and calibration.
Model Safety and Robustness
- Detecting unsafe or harmful model outputs.
- Handling adversarial inputs effectively.
- Conducting red teaming and stress testing on fine-tuned models.
Auditing and Monitoring AI Systems
- Metrics for evaluating bias and fairness, such as demographic parity.
- Tools for explainability and transparency frameworks.
- Best practices for ongoing monitoring and governance.
Toolkits and Hands-On Practice
- Utilizing open-source libraries like 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 and model cards for compliance purposes.
- Preparing for audits and external reviews.
Summary and Next Steps
Requirements
- Knowledge of machine learning models and their training procedures.
- 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.
- Machine learning engineers.
Open Training Courses require 5+ participants.
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