Auditing Artificial Intelligence Models for Bias and Accuracy

Introduction to AI Auditing

• Overview of AI Systems: Types, applications, and decision-making processes.
• Importance of Auditing: Ensuring fairness, compliance, and ethical integrity.
• Key Stakeholders: Data scientists, auditors, regulators, and business leaders.

Understanding AI Bias and Fairness

• Types of Bias: Data bias, algorithmic bias, and human-introduced bias.
• Impacts of Bias: Ethical, operational, and reputational risks.
• Fairness Metrics: Measuring and evaluating bias in AI models.

AI Model Validation and Accuracy Assessment

• Performance Metrics: Accuracy, precision, recall, F1 score, and ROC curves.
• Validation Techniques: Cross-validation, testing on hold-out datasets, and benchmarking.
• Model Explainability: Understanding decision processes using interpretable AI techniques.

Data Governance and Preprocessing

• Data Quality Assessment: Completeness, consistency, and representativeness.
• Data Cleaning and Transformation: Reducing noise and bias in datasets.
• Documentation and Traceability: Ensuring reproducibility and accountability.

Auditing Methodologies and Tools

• Audit Frameworks: Step-by-step approaches for AI auditing.
• Tools and Software: Python libraries, AI monitoring platforms, and dashboards.
• Risk-Based Auditing: Prioritizing high-impact models and decisions.

Regulatory and Ethical Considerations

• AI Governance Guidelines: Local and international frameworks.
• Ethical Principles: Transparency, accountability, and human oversight.
• Compliance Requirements: Data protection, algorithmic accountability, and reporting obligations.

1ST BATCH: Tuesday, January 27, 2026 — Friday, January 30, 2026.

2ND BATCH: Tuesday, May 19, 2026 — Friday, May 22, 2026.

3RD BATCH: Tuesday, September 15, 2026 — Friday, September 18, 2026.

The training methodology integrates lectures, interactive discussions, collaborative group exercises, and
illustrative examples. Participants will acquire a blend of theoretical insights and hands-on practical
experience, emphasizing the application of learned techniques. This approach ensures that attendees return
to their professional environments equipped with both the competence and self-assurance to effectively
implement the acquired skills in their responsibilities.