Safety and Bias Mitigation in Fine-Tuned Models Training Course
As AI increasingly becomes integral to decision-making processes across various industries, ensuring the safety and mitigating bias in fine-tuned models has emerged as a critical concern, particularly as regulatory standards continue to evolve.
This live, instructor-led training, available either online or onsite, is designed for intermediate-level ML engineers and AI compliance professionals seeking to identify, assess, and mitigate safety risks and biases within fine-tuned language models.
Upon completing this training, participants will be equipped to:
- Comprehend the ethical and regulatory landscape governing safe AI systems.
- Detect and evaluate prevalent types of bias in fine-tuned models.
- Implement bias mitigation strategies both during and after the training phase.
- Design and audit models to ensure safety, transparency, and fairness.
Course Format
- Interactive lectures and facilitated discussions.
- Extensive exercises and practical application.
- Hands-on implementation within a live-lab environment.
Customization Options
- Contact us to arrange a customized training version of this course.
Course Outline
Foundations of Safe and Fair AI
- Core concepts: safety, bias, fairness, and transparency
- Categories of bias: dataset, representation, and algorithmic
- Overview of key regulatory frameworks, including the EU AI Act and GDPR
Bias in Fine-Tuned Models
- Mechanisms by which fine-tuning may introduce or amplify bias
- Analysis of case studies and real-world failures
- Methods for identifying bias in datasets and model predictions
Techniques for Bias Mitigation
- Data-level strategies, such as rebalancing and augmentation
- In-training approaches, including regularization and adversarial debiasing
- Post-processing methods, like output filtering and calibration
Model Safety and Robustness
- Identification of unsafe or harmful outputs
- Managing adversarial inputs
- 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
- Explainability tools and transparency frameworks
- Establishing ongoing monitoring and governance practices
Toolkits and Hands-On Practice
- Utilizing open-source libraries like 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
- Creating documentation and model cards for compliance purposes
- Preparation for audits and external reviews
Summary and Next Steps
Requirements
- A solid understanding of machine learning models and training processes
- Practical experience with fine-tuning and LLMs
- Familiarity with Python and NLP concepts
Audience
- AI compliance teams
- ML engineers
Open Training Courses require 5+ participants.
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