AI Audit & Compliance
Master the art and science of auditing AI systems. Learn audit frameworks, technical auditing techniques, bias audits, model cards, datasheets, NYC Local Law 144, and algorithmic auditing methodologies for accountable AI deployment.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
What is AI auditing, why it matters, types of AI audits, and the growing regulatory demand for algorithmic accountability.
2. Audit Framework
Structured approach to AI auditing: planning, scoping, evidence collection, analysis, reporting, and follow-up procedures.
3. Technical Audit
Auditing model performance, data quality, security controls, infrastructure, and deployment pipelines.
4. Bias Audit
NYC Local Law 144, disparate impact analysis, fairness metrics, intersectional testing, and bias audit reporting.
5. Documentation
Model cards, datasheets for datasets, audit reports, compliance evidence, and regulatory submission requirements.
6. Best Practices
Building an audit program, auditor independence, continuous auditing, and preparing for regulatory examinations.
What You'll Learn
By the end of this course, you'll be able to:
Plan AI Audits
Design and execute structured AI audits covering technical performance, fairness, security, and compliance dimensions.
Conduct Bias Audits
Perform bias audits compliant with NYC Local Law 144 and other emerging regulations using statistical fairness methodologies.
Document Findings
Create comprehensive audit reports, model cards, and datasheets that meet regulatory requirements and stakeholder expectations.
Ensure Compliance
Map AI audit findings to regulatory requirements and build sustainable compliance programs for algorithmic accountability.