4 Months Intensive Program
AI Ethics & Compliance
Ensure responsible AI development with expertise in ethics, governance, and regulatory compliance for trustworthy AI systems
4 Months
Duration
4-5 hrs/week
Weekly Coaching
15-40
Batch Size
$999
Course Fee
Learning Path
Responsible-AI Principles
→
Trustworthy-AI Practice Track
Course Structure
Months 1-2
Ethical Frameworks & Fairness Metrics
Learning Outcomes
- ✓Understand core Responsible AI principles and international ethical guidelines
- ✓Identify and define common bias types in datasets and models
- ✓Apply quantitative fairness metrics to assess AI systems
Weekly Topics
- • Responsible AI principles: fairness, accountability, transparency, and explainability
- • Global frameworks (EU AI Act, OECD, UNESCO AI ethics guidelines)
- • Bias taxonomy: sampling bias, label bias, historical bias, proxy bias
- • Fairness metrics: statistical parity, equalized odds, disparate impact
- • Accuracy–fairness trade-offs in real-world applications
Practical Work
- • Conduct a fairness audit on a sample dataset
- • Create a comparative fairness metrics report
Assessment
- • Weekly quizzes on ethics frameworks & fairness metrics
- • Submission of Fairness Metrics Report (peer-reviewed)
Months 3-4
Bias Testing Labs & Transparency Reports
Learning Outcomes
- ✓Use open-source tools to detect and measure bias in AI models
- ✓Develop transparency documentation such as model cards and datasheets
- ✓Implement explainability techniques for fairness audits
Weekly Topics
- • Bias detection tools: AIF360, Fairlearn, Google's What-If Tool
- • Designing and running bias testing pipelines
- • Model cards and datasheets for datasets
- • Explainability techniques (LIME, SHAP) applied to fairness checks
- • Case studies on bias in hiring, lending, and facial recognition systems
Practical Work
- • Perform a bias audit using open-source toolkits
- • Publish a model card for a tested ML model
Assessment
- • Mid-term bias audit submission & presentation
- • Peer review of model card documentation
Months 3-4 (Final Phase)
Regulation Mapping & Mitigation Playbook
Learning Outcomes
- ✓Map and interpret AI regulations (GDPR, CCPA, upcoming AI laws)
- ✓Build risk registers for AI projects
- ✓Develop a mitigation playbook for bias reduction in AI pipelines
Weekly Topics
- • Regulation mapping: GDPR, CCPA, and the EU AI Act
- • Creating and maintaining AI risk registers
- • Bias mitigation strategies: pre-processing, in-processing, post-processing
- • Operationalizing Responsible AI in organizations
- • Communicating risk and mitigation plans to executives & regulators
Practical Work
- • Draft an AI risk register for a chosen use case
- • Develop a bias mitigation playbook for deployment-ready models
Assessment
- • Final project: Bias Audit + Model Card + Risk Register
- • Peer-reviewed mitigation playbook
- • Capstone presentation to the evaluation panel
Deliverables for Learners
Projects
- •Fairness Metrics Report
- •Bias Audit Report
- •Model Card Documentation
- •AI Risk Register
- •Bias Mitigation Playbook
Certifications
Responsible-AI Certificate on course completion with comprehensive assessment of ethics and compliance knowledge
Resources
- •Fairness metrics templates
- •Model card & datasheet templates
- •Regulation mapping guides
- •Recorded coaching sessions
Tools & Frameworks You'll Master
AIF360
Bias Detection
Fairlearn
Fairness Metrics
LIME
Explainability
SHAP
Model Interpretation
What-If Tool
Google AI
Regulatory Frameworks Covered
EU AI Act
European Union AI Regulation
GDPR
Data Protection Regulation
CCPA
California Consumer Privacy Act
UNESCO
AI Ethics Guidelines
Ready to Build Trustworthy AI Systems?
Join our comprehensive 4-month program and become an AI Ethics & Compliance expert with hands-on experience in bias detection, fairness metrics, and regulatory compliance.