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 standards continue to evolve, the issue of safety and bias mitigation in fine-tuned models has emerged as a critical concern.
This instructor-led, live training (available online or onsite) is designed for intermediate-level ML engineers and AI compliance professionals who seek to identify, evaluate, and reduce safety risks and biases within fine-tuned language models.
Upon completion of this training, participants will be able to:
- Grasp the ethical and regulatory framework underpinning safe AI systems.
- Identify and assess common forms of bias prevalent in fine-tuned models.
- Implement bias mitigation techniques both during and after the training phase.
- Design and audit models to ensure safety, transparency, and fairness.
Course Format
- Interactive lectures and group discussions.
- Extensive exercises and practical application.
- Hands-on implementation within a live-lab environment.
Customization Options
- To arrange customized training for this course, please contact us directly.
Course Outline
Foundations of Safe and Fair AI
- Core concepts: safety, bias, fairness, transparency.
- Bias typologies: dataset, representation, and algorithmic bias.
- Overview of regulatory frameworks (e.g., EU AI Act, GDPR).
Bias in Fine-Tuned Models
- Mechanisms through which fine-tuning introduces or amplifies bias.
- Case studies and real-world failure examples.
- Methods for identifying bias in datasets and model predictions.
Techniques for Bias Mitigation
- Data-level strategies (rebalancing, augmentation).
- Training-stage strategies (regularization, adversarial debiasing).
- Post-processing strategies (output filtering, calibration).
Model Safety and Robustness
- Detection of unsafe or harmful outputs.
- Handling adversarial inputs.
- Red teaming and stress testing of fine-tuned models.
Auditing and Monitoring AI Systems
- Evaluation metrics for bias and fairness (e.g., demographic parity).
- Explainability tools and transparency frameworks.
- 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 via 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.
- Preparation for audits and external reviews.
Summary and Next Steps
Requirements
- A foundational understanding 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.
Audience
- AI compliance teams.
- ML engineers.
Open Training Courses require 5+ participants.
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