Safety and Bias Mitigation in Fine-Tuned Models Training Course
As AI systems become increasingly integral to decision-making processes across various sectors and regulatory landscapes continue to shift, ensuring safety and mitigating bias in fine-tuned models has become a critical priority.
This live, instructor-led training, available online or onsite, is designed for intermediate-level ML engineers and AI compliance professionals seeking to pinpoint, assess, and minimize safety risks and biases inherent in fine-tuned language models.
Upon completion of this program, participants will be equipped to:
- Grasp the ethical and regulatory frameworks governing safe AI systems.
- Detect and assess prevalent forms of bias within fine-tuned models.
- Implement bias mitigation strategies both during and following the training phase.
- Develop and audit models with a focus on safety, transparency, and fairness.
Course Format
- Engaging lectures and collaborative discussions.
- Extensive exercises and practical drills.
- Hands-on implementation within a live-lab environment.
Customization Options
- To tailor this training to your specific needs, please reach out to us for arrangements.
Course Outline
Foundations of Safe and Fair AI
- Core concepts: safety, bias, fairness, and transparency.
- Categorization of bias: dataset, representation, and algorithmic.
- Overview of relevant regulatory frameworks, including the EU AI Act and GDPR.
Bias in Fine-Tuned Models
- Mechanisms by which fine-tuning can introduce or exacerbate bias.
- Analysis of case studies and real-world failure scenarios.
- Techniques for identifying bias within datasets and model predictions.
Techniques for Bias Mitigation
- Data-level strategies, such as rebalancing and augmentation.
- In-training methods, including regularization and adversarial debiasing.
- Post-processing approaches, such as output filtering and calibration.
Model Safety and Robustness
- Methods for detecting unsafe or harmful model outputs.
- Strategies for handling adversarial inputs.
- Red teaming and stress testing procedures for fine-tuned models.
Auditing and Monitoring AI Systems
- Evaluation metrics for bias and fairness, such as demographic parity.
- Utilization of explainability tools and transparency frameworks.
- Best practices for ongoing monitoring and governance.
Toolkits and Hands-On Practice
- Application of open-source libraries, including 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 to meet compliance standards.
- Preparing for internal audits and external reviews.
Summary and Next Steps
Requirements
- A solid understanding of machine learning models and their training methodologies.
- Practical experience with fine-tuning and Large Language Models (LLMs).
- Proficiency in Python and core Natural Language Processing (NLP) concepts.
Target Audience
- AI compliance teams.
- ML engineers.
Open Training Courses require 5+ participants.
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