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 develop, ensuring safety and mitigating bias in fine-tuned models has become a critical priority.
This instructor-led live training session, available online or onsite, is designed for machine learning engineers and AI compliance professionals at an intermediate level who aim to identify, assess, and minimise safety risks and biases within fine-tuned language models.
Upon completion of this training, participants will be equipped to:
- Grasp the ethical and regulatory landscape governing safe AI systems.
- Recognise and evaluate prevalent forms of bias found in fine-tuned models.
- Implement bias mitigation strategies both during and post-training phases.
- Design and audit models with a focus on safety, transparency, and fairness.
Course Format
- Engaging lectures and interactive discussions.
- Ample exercises and practical application.
- Live laboratory sessions for hands-on implementation.
Customisation Options
- For bespoke training requirements, please contact us to arrange a tailored session.
Course Outline
Foundations of Safe and Fair AI
- Core concepts: safety, bias, fairness, and transparency
- Types of bias: dataset, representation, and algorithmic
- Overview of regulatory frameworks such as the EU AI Act and GDPR
Bias in Fine-Tuned Models
- Understanding how fine-tuning 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 including rebalancing and augmentation
- In-training strategies such as 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
- Evaluation metrics for bias and fairness (e.g., demographic parity)
- Explainability tools and transparency frameworks
- Best practices for ongoing monitoring and governance
Toolkits and Hands-On Practice
- Utilising open-source libraries such as Fairlearn, Transformers, and CheckList
- Practical exercise: Detecting and mitigating bias in a fine-tuned model
- Generating safe outputs through strategic prompt design and constraints
Enterprise Use Cases and Compliance Readiness
- Best practices for integrating safety 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 in fine-tuning and working with 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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