Safety and Bias Mitigation in Fine-Tuned Models Training Course
As AI systems become increasingly integrated into critical decision-making processes across various sectors, ensuring the safety and mitigation of bias in fine-tuned models has emerged as a vital priority, particularly with the ongoing evolution of regulatory standards.
This live, instructor-led training, available online or onsite, is specifically designed for intermediate-level ML engineers and AI compliance professionals. It provides a comprehensive framework for identifying, assessing, and minimizing safety risks and biases within fine-tuned language models.
Upon completion of this course, participants will be equipped to:
- Grasp the ethical and regulatory landscape governing safe AI systems.
- Detect and assess common bias patterns within fine-tuned models.
- Implement effective bias mitigation strategies both during and after the training phase.
- Architect and audit models with a focus on safety, transparency, and fairness.
Course Format
- Engaging lectures combined with facilitated discussions.
- Extensive exercises and practical application.
- Real-time implementation within a live laboratory environment.
Customization Options
- For organizations requiring a tailored training experience, please reach out to us to discuss specific arrangements.
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 can introduce or exacerbate bias
- Analysis of case studies and real-world failure scenarios
- Methods for identifying bias within datasets and model predictions
Techniques for Bias Mitigation
- Data-level approaches, such as rebalancing and augmentation
- In-training methods, including regularization and adversarial debiasing
- Post-processing techniques, such as output filtering and calibration
Model Safety and Robustness
- Detection of unsafe or harmful model outputs
- Strategies for handling adversarial inputs
- Red teaming and stress testing fine-tuned models
Auditing and Monitoring AI Systems
- Evaluation metrics for bias and fairness, such as demographic parity
- Leveraging explainability tools and transparency frameworks
- Implementing ongoing monitoring and governance practices
Toolkits and Hands-On Practice
- Utilization of open-source libraries like Fairlearn, Transformers, and CheckList
- Practical exercise: Detecting and mitigating bias in a fine-tuned model
- Generating safe outputs via prompt design and constraint application
Enterprise Use Cases and Compliance Readiness
- Best practices for integrating safety protocols into LLM workflows
- Documentation standards, including model cards for compliance
- Preparing for internal audits and external reviews
Summary and Next Steps
Requirements
- A solid understanding of machine learning models and training processes
- Practical experience in fine-tuning and working with LLMs
- Familiarity with Python and NLP concepts
Target Audience
- AI compliance teams
- ML engineers
Open Training Courses require 5+ participants.
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