Auditing Artificial Intelligence Models for Bias and Accuracy

• Equip participants with knowledge and tools to evaluate AI models for fairness, bias, and accuracy
• Strengthen understanding of ethical, legal, and governance requirements in AI deployment
• Enhance skills in auditing AI systems used in decision-making processes
• Promote responsible, transparent, and accountable use of artificial intelligence

Course Content

Introduction to AI Auditing

• Overview of artificial intelligence and machine learning models
• Importance of auditing AI systems
• Risks associated with biased or inaccurate AI models
• Role of auditors, compliance officers, and regulators

Understanding Bias in AI Models

• Types of bias: data, algorithmic, and societal bias
• Sources of bias in training data and model design
• Impact of bias on decision-making and stakeholders
• Regulatory and ethical considerations

Accuracy and Model Performance Evaluation

• Model validation and testing techniques
• Key accuracy metrics and performance indicators
• Overfitting, underfitting, and model robustness
• Stress testing and scenario analysis

AI Audit Methodologies and Tools

• Frameworks and standards for AI auditing
• Explainability and transparency techniques
• Using audit tools and model documentation
• Version control and audit trails

Governance, Risk, and Compliance

• AI governance structures and policies
• Legal and regulatory requirements for AI systems
• Data privacy and security considerations
• Managing operational and reputational risks

DATE:
1ST BATCH: 27th–30th Jan, 2026
2ND BATCH: 19th–22nd May, 2026
3RDBATCH:  15th–18th Sep, 2026