Safety and Bias Mitigation in Fine-Tuned Models Training Course
As AI systems become increasingly integral to decision-making across various sectors and regulatory landscapes continue to evolve, addressing safety and bias in fine-tuned models has become a critical priority.
This instructor-led, live training session, available both online and onsite, is designed for intermediate-level machine learning engineers and AI compliance professionals. The course aims to equip participants with the skills to identify, assess, and mitigate safety risks and biases inherent in fine-tuned language models.
Upon completion of this training, participants will be capable of:
- Gaining a comprehensive understanding of the ethical and regulatory frameworks governing safe AI systems.
- Recognising and evaluating prevalent forms of bias within fine-tuned models.
- Implementing bias mitigation strategies both during the training phase and post-training.
- Designing and auditing models to ensure safety, transparency, and fairness.
Course Format
- Engaging interactive lectures and discussions.
- Extensive practical exercises and hands-on practice.
- Real-world implementation within a live laboratory environment.
Customization Options
- To arrange a customized training session for this course, please contact us.
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 (e.g., EU AI Act, GDPR).
Bias in Fine-Tuned Models
- How fine-tuning processes can introduce or exacerbate bias.
- Case studies and real-world failures.
- Techniques for identifying bias in datasets and model predictions.
Techniques for Bias Mitigation
- Data-level strategies (e.g., rebalancing, augmentation).
- In-training strategies (e.g., regularization, adversarial debiasing).
- Post-processing strategies (e.g., output filtering, calibration).
Model Safety and Robustness
- Detecting unsafe or harmful outputs.
- Handling adversarial inputs.
- Red teaming and stress testing fine-tuned models.
Auditing and Monitoring AI Systems
- Metrics for bias and fairness evaluation (e.g., demographic parity).
- Explainability tools and transparency frameworks.
- Best practices for ongoing monitoring and governance.
Toolkits and Hands-On Practice
- Leveraging open-source libraries (e.g., Fairlearn, Transformers, CheckList).
- Hands-on 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 into LLM workflows.
- Documentation and model cards for compliance purposes.
- Preparing for audits and external reviews.
Summary and Next Steps
Requirements
- A solid understanding of machine learning models and their training processes.
- 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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