Overview What You Will Learn Curriculum Instructor

Course Overview

Artificial Intelligence (AI) and Machine Learning (ML) systems are increasingly used to make critical decisions across industries, including healthcare, finance, energy, and transportation. Algorithmic auditing helps organizations evaluate these systems for fairness, transparency, accountability, security, and regulatory compliance. By identifying potential risks, biases, and vulnerabilities, algorithmic auditing supports responsible AI adoption and strengthens trust in automated decision-making.

This course begins with an introduction to AI and ML fundamentals, model behavior, and the principles of algorithmic auditing. It explores ethical and legal considerations, including GDPR Article 22, the EU AI Act, the NIST AI Risk Management Framework, and ISO/IEC 42001. The course also covers bias detection and mitigation, fairness metrics, explainable AI techniques such as SHAP and LIME, adversarial robustness, and auditing critical infrastructure across healthcare, energy, and financial services. Hands-on labs and case studies demonstrate practical auditing techniques using tools such as Fairlearn and AI Fairness 360, along with end-to-end audit implementation, compliance reporting, continuous monitoring, and AI governance frameworks.

Develop practical skills to audit AI systems, detect bias, evaluate model transparency and robustness, assess regulatory compliance, and implement responsible AI governance.

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What You Will Learn

  • Implement comprehensive algorithmic audits that assess AI systems for bias, fairness, transparency, and regulatory compliance across the full model lifecycle from design to deployment to monitoring.
  • Identify and measure algorithmic bias using established fairness metrics (demographic parity, equalized odds, predictive parity) and understand the trade-offs between different fairness definitions.
  • Apply legal frameworks including GDPR Article 22, EU AI Act, US Algorithmic Accountability Act, and NIST AI RMF to ensure AI systems meet regulatory requirements and organizational risk tolerances.
  • Use industry-standard auditing tools (Fairlearn with interactive dashboard, AI Fairness 360 with 70+ metrics, Aequitas) to detect bias, evaluate model behavior across demographic groups, and generate compliance reports.
  • Select optimal XAI methods (SHAP vs LIME vs Integrated Gradients) using empirical decision matrices for faithfulness vs stability vs interpretability trade-offs.
  • Detect adversarial attacks using forensically validated multi-method pipelines (TDA + SHAP + Benford’s Law + model inversion) proven on real datasets.
  • Analyze real-world AI failures (COMPAS 45% FPR disparity, Amazon gender bias, Mobley v. Workday 2025 litigation) to understand root causes and apply evidence-backed preventive controls.

Program Curriculum

Chapter 1: Introduction to Machine Learning and AI Models 3 Videos

0 hrs 31 mins What is Artificial Intelligence? Machine Learning Fundamentals Understanding Model Behavior in Decision-making Chapter 1 Quiz More Details Chapter 2: Introduction to Algorithmic Auditing 3 Videos

0 hrs 28 mins What is Algorithmic Auditing? Benefits of Auditing Algorithms Key Challenges and Industry Gaps Chapter 2 Quiz More Details Chapter 3: Ethical and Legal Considerations in AI 3 Videos

0 hrs 25 mins Ethical AI Principles and Responsible AI Introduction to Legal Accountability in AI Role of Frameworks in Promoting Trust Chapter 3 Quiz More Details Chapter 4: Current Legal Landscape 6 Videos

0 hrs 37 mins GDPR Article 22 - Automated Decision-Making EU AI Act and High-Risk Systems U.S. Algorithmic Accountability Act (2023) NIST AI Risk Management Framework ISO/IEC 42001 - Certifiable AI Management Compliance Report Generation (Hands-On Lab) Chapter 4 Quiz More Details Chapter 5: Bias and Fairness in Machine Learning 8 Videos

1 hr 3 mins Understanding Bias in ML Systems Fairness Metrics and Mathematical Definitions Proxy Variables and Indirect Discrimination COMPAS Case Study: A Deep Dive Bias Mitigation Techniques Threshold Optimization for Fairness Hands-On Bias Detection Lab Section Summary Chapter 5 Quiz More Details Chapter 6: Explainable AI and Transparency (XAI) 9 Videos

0 hrs 57 mins Introduction to XAI SHAP: Theory and Application LIME: Local Interpretable Model-agnostic Explanations SHAP vs. LIME: Selection Criteria Admissibility and Ethical Obligations Model Cards and Datasheets for Datasets Hands-On SHAP Lab Hands-On SHAP vs. LIME Comparison Section Summary Chapter 6 Quiz More Details Chapter 7: Adversarial Robustness and Security 6 Videos

0 hrs 49 mins Adversarial Attack Taxonomy Adversarial Robustness Evaluation Forensic Detection of Poisoning Attacks Defenses and Mitigation Adversarial Attack Lab (Hands-On) Section Summary Chapter 7 Quiz More Details Chapter 8: Auditing Critical Infrastructure 6 Videos

0 hrs 45 mins Critical Infrastructure AI Risks Healthcare AI Auditing Power Grid AI and NERC-CIP Compliance Financial Systems AI Auditing Healthcare AI Audit Walkthrough Section Summary Chapter 8 Quiz More Details Chapter 9: Practical Auditing Implementation 7 Videos

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Instructor

Danyal Khan

Danyal Sage Khan is a cybersecurity researcher, systems architect, and AI governance specialist. A Marie Skłodowska-Curie PhD Scholar at Universidad de Granada and ISO 27001 Lead Auditor, his research focuses on secure AI for critical infrastructure. He is the author of Open for Business and contributes to the DataOps Landscape Initiative. His experience across enterprise security, military operations, and advanced research shapes his practical, threat-aware approach to AI security and algorithmic auditing.

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