- An understanding of machine learning models and training processes
- Experience working with fine-tuning and LLMs
- Familiarity with Python and NLP concepts
Audience
- AI compliance teams
- ML engineers
Safety and Bias Mitigation in Fine-Tuned Models is a growing concern as AI becomes more embedded in decision-making across industries and regulatory standards continue to evolve.
This instructor-led, live training (online or onsite) is aimed at intermediate-level ML engineers and AI compliance professionals who wish to identify, evaluate, and reduce safety risks and biases in fine-tuned language models.
By the end of this training, participants will be able to:
- Understand the ethical and regulatory context for safe AI systems.
- Identify and evaluate common forms of bias in fine-tuned models.
- Apply bias mitigation techniques during and after training.
- Design and audit models for safety, transparency, and fairness.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Foundations of Safe and Fair AI
- Key concepts: safety, bias, fairness, transparency
- Types of bias: dataset, representation, algorithmic
- Overview of regulatory frameworks (EU AI Act, GDPR, etc.)
Bias in Fine-Tuned Models
- How fine-tuning can introduce or amplify bias
- Case studies and real-world failures
- Identifying bias in datasets and model predictions
Techniques for Bias Mitigation
- Data-level strategies (rebalancing, augmentation)
- In-training strategies (regularization, adversarial debiasing)
- Post-processing strategies (output filtering, calibration)
Model Safety and Robustness
- Detecting unsafe or harmful outputs
- Adversarial input handling
- Red teaming and stress testing fine-tuned models
Auditing and Monitoring AI Systems
- Bias and fairness evaluation metrics (e.g., demographic parity)
- Explainability tools and transparency frameworks
- Ongoing monitoring and governance practices
Toolkits and Hands-On Practice
- Using open-source libraries (e.g., Fairlearn, Transformers, CheckList)
- Hands-on: Detecting and mitigating bias in a fine-tuned model
- Generating safe outputs through prompt design and constraints
Enterprise Use Cases and Compliance Readiness
- Best practices for integrating safety in LLM workflows
- Documentation and model cards for compliance
- Preparing for audits and external reviews
Summary and Next Steps
United Arab Emirates - Safety and Bias Mitigation in Fine-Tuned Models
Qatar - Safety and Bias Mitigation in Fine-Tuned Models
Egypt - Safety and Bias Mitigation in Fine-Tuned Models
Saudi Arabia - Safety and Bias Mitigation in Fine-Tuned Models
South Africa - Safety and Bias Mitigation in Fine-Tuned Models
Brasil - Safety and Bias Mitigation in Fine-Tuned Models
Canada - Safety and Bias Mitigation in Fine-Tuned Models
中国 - Safety and Bias Mitigation in Fine-Tuned Models
香港 - Safety and Bias Mitigation in Fine-Tuned Models
澳門 - Safety and Bias Mitigation in Fine-Tuned Models
台灣 - Safety and Bias Mitigation in Fine-Tuned Models
USA - Safety and Bias Mitigation in Fine-Tuned Models
Österreich - Safety and Bias Mitigation in Fine-Tuned Models
Schweiz - Safety and Bias Mitigation in Fine-Tuned Models
Deutschland - Safety and Bias Mitigation in Fine-Tuned Models
Czech Republic - Safety and Bias Mitigation in Fine-Tuned Models
Denmark - Safety and Bias Mitigation in Fine-Tuned Models
Estonia - Safety and Bias Mitigation in Fine-Tuned Models
Finland - Safety and Bias Mitigation in Fine-Tuned Models
Greece - Safety and Bias Mitigation in Fine-Tuned Models
Magyarország - Safety and Bias Mitigation in Fine-Tuned Models
Ireland - Safety and Bias Mitigation in Fine-Tuned Models
Luxembourg - Safety and Bias Mitigation in Fine-Tuned Models
Latvia - Safety and Bias Mitigation in Fine-Tuned Models
España - Safety and Bias Mitigation in Fine-Tuned Models
Italia - Safety and Bias Mitigation in Fine-Tuned Models
Lithuania - Safety and Bias Mitigation in Fine-Tuned Models
Nederland - Safety and Bias Mitigation in Fine-Tuned Models
Norway - Safety and Bias Mitigation in Fine-Tuned Models
Portugal - Safety and Bias Mitigation in Fine-Tuned Models
România - Safety and Bias Mitigation in Fine-Tuned Models
Sverige - Safety and Bias Mitigation in Fine-Tuned Models
Türkiye - Safety and Bias Mitigation in Fine-Tuned Models
Malta - Safety and Bias Mitigation in Fine-Tuned Models
Belgique - Safety and Bias Mitigation in Fine-Tuned Models
France - Safety and Bias Mitigation in Fine-Tuned Models
日本 - Safety and Bias Mitigation in Fine-Tuned Models
Australia - Safety and Bias Mitigation in Fine-Tuned Models
Malaysia - Safety and Bias Mitigation in Fine-Tuned Models
New Zealand - Safety and Bias Mitigation in Fine-Tuned Models
Philippines - Safety and Bias Mitigation in Fine-Tuned Models
Singapore - Safety and Bias Mitigation in Fine-Tuned Models
Thailand - Safety and Bias Mitigation in Fine-Tuned Models
Vietnam - Safety and Bias Mitigation in Fine-Tuned Models
India - Safety and Bias Mitigation in Fine-Tuned Models
Argentina - Safety and Bias Mitigation in Fine-Tuned Models
Chile - Safety and Bias Mitigation in Fine-Tuned Models
Costa Rica - Safety and Bias Mitigation in Fine-Tuned Models
Ecuador - Safety and Bias Mitigation in Fine-Tuned Models
Guatemala - Safety and Bias Mitigation in Fine-Tuned Models
Colombia - Safety and Bias Mitigation in Fine-Tuned Models
México - Safety and Bias Mitigation in Fine-Tuned Models
Panama - Safety and Bias Mitigation in Fine-Tuned Models
Peru - Safety and Bias Mitigation in Fine-Tuned Models
Uruguay - Safety and Bias Mitigation in Fine-Tuned Models
Venezuela - Safety and Bias Mitigation in Fine-Tuned Models
Polska - Safety and Bias Mitigation in Fine-Tuned Models
United Kingdom - Safety and Bias Mitigation in Fine-Tuned Models
South Korea - Safety and Bias Mitigation in Fine-Tuned Models
Pakistan - Safety and Bias Mitigation in Fine-Tuned Models
Sri Lanka - Safety and Bias Mitigation in Fine-Tuned Models
Bulgaria - Safety and Bias Mitigation in Fine-Tuned Models
Bolivia - Safety and Bias Mitigation in Fine-Tuned Models
Indonesia - Safety and Bias Mitigation in Fine-Tuned Models
Kazakhstan - Safety and Bias Mitigation in Fine-Tuned Models
Moldova - Safety and Bias Mitigation in Fine-Tuned Models
Morocco - Safety and Bias Mitigation in Fine-Tuned Models
Tunisia - Safety and Bias Mitigation in Fine-Tuned Models
Kuwait - Safety and Bias Mitigation in Fine-Tuned Models
Oman - Safety and Bias Mitigation in Fine-Tuned Models
Slovakia - Safety and Bias Mitigation in Fine-Tuned Models
Kenya - Safety and Bias Mitigation in Fine-Tuned Models
Nigeria - Safety and Bias Mitigation in Fine-Tuned Models
Botswana - Safety and Bias Mitigation in Fine-Tuned Models
Slovenia - Safety and Bias Mitigation in Fine-Tuned Models
Croatia - Safety and Bias Mitigation in Fine-Tuned Models
Serbia - Safety and Bias Mitigation in Fine-Tuned Models
Bhutan - Safety and Bias Mitigation in Fine-Tuned Models
Nepal - Safety and Bias Mitigation in Fine-Tuned Models
Uzbekistan - Safety and Bias Mitigation in Fine-Tuned Models