Overview

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This course provides a structured, practitioner-focused approach to identifying, managing, and governing risks in AI systems across their lifecycle. It equips learners with the tools to move beyond model performance and address real-world concerns such as bias, model degradation, regulatory exposure, and operational accountability. Learners begin by diagnosing bias in datasets and models, applying fairness metrics, and conducting audits that reveal hidden disparities across demographic groups. The course then advances to bias mitigation, where participants explore practical techniques across the model pipeline and learn to navigate trade-offs between fairness and performance. The course expands into production environments, teaching how to design monitoring pipelines that detect data drift, concept drift, and performance degradation before they impact business outcomes. Learners connect these monitoring signals to structured risk evaluation frameworks, translating technical anomalies into enterprise risk language using scoring models, risk registers, and response strategies aligned with standards such as ISO 31000 and COSO ERM. Finally, the course integrates AI systems into broader governance and compliance structures. Participants learn to map AI use cases to regulatory obligations (e.g., GDPR, EU AI Act), build compliance inventories, and design governance dashboards that support audit readiness and executive oversight. By the end of the course, learners will be able to operationalize AI risk management, implement continuous monitoring, prioritize and respond to model risks, and align AI systems with organizational and regulatory expectations.

Syllabus

Bias and Fairness Analysis in AI Models

AI systems trained on biased data produce biased outcomes — and in regulated domains like credit, hiring, and healthcare, those outcomes carry legal and reputational consequences. This module equips you to move from awareness of bias to concrete action. You will learn how to detect and measure bias in datasets using statistical tests and group fairness metrics such as demographic parity and equalized odds, and how to make those findings visible through bias dashboards. You will then apply pre-processing and post-processing mitigation techniques and evaluate the trade-offs between fairness improvements, model performance, and regulatory compliance. By the end of this module, you will be able to identify, quantify, and mitigate bias in AI models while documenting your decisions for audit and governance review.

Monitor and Manage Model Risk

In this module, you focus on how AI systems are monitored and managed after deployment to ensure they remain reliable, compliant, and aligned with business objectives. You will learn how to build monitoring pipelines that detect data and concept drift, connect model behavior to business metrics, and trigger alerts based on defined risk thresholds. You will also examine how to evaluate and prioritize risks using structured scoring frameworks and integrate model issues into enterprise risk registers. By the end of this module, you will be able to design monitoring systems and translate model anomalies into actionable, governance-aligned risk responses.

AI Governance Integration with Risk Management

In this module, you focus on integrating AI governance into enterprise risk and compliance systems that already guide business decisions. You will learn how to embed AI policies into established frameworks such as COSO and ISO 31000, ensuring that model risks are visible within risk registers, appetite statements, and control processes. You will also build structured compliance maps that connect AI systems to regulatory requirements like the EU AI Act and GDPR, and translate this information into executive dashboards using governance KPIs. By the end of this module, you will be able to align AI governance with enterprise risk processes and communicate compliance and risk posture to leadership with clarity.