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AI Governance, Ethics & Responsible AI — Managing AI Risks, Ethics, and Trust

AI Governance, Ethics & Responsible AI examines how organizations can innovate with artificial intelligence while maintaining accountability, trust, and effective control. The program connects ethical principles with practical governance across the AI lifecycle, covering risk classification, fairness, privacy, transparency, security, human oversight, third-party AI, and ongoing monitoring. Participants work with governance frameworks and realistic cases to define decision rights, controls, escalation mechanisms, and leadership responsibilities that support responsible and sustainable AI adoption.

Montreal

5 days

October 26, 2026 – October 30, 2026

Price

EUR 6,500

Initial Enrollment Request

Learning Objectives

Explain the principles and organizational responsibilities behind responsible and trustworthy AI.
Identify ethical, legal, operational, security, and reputational risks across the AI lifecycle.
Classify AI use cases by risk and determine appropriate governance and human oversight.
Establish roles, decision rights, policies, documentation, and escalation mechanisms for AI governance.
Evaluate fairness, transparency, privacy, third-party, and generative-AI risks using practical controls.
Design monitoring and review practices that maintain accountability after AI systems are deployed.

Detailed Daily Schedule

Day 1 — Responsible AI Foundations and Governance Principles

Define responsible AI, trustworthy AI, ethics, governance, and risk management.
Examine fairness, accountability, transparency, privacy, safety, and human agency.
Map stakeholder impacts across the AI lifecycle.
Explore how organizational values translate into governance requirements.
Establish the leadership responsibilities behind responsible AI adoption.

Day 2 — AI Risk Identification and Classification

Identify risks arising from data, models, use context, users, and third parties.
Examine bias, discrimination, privacy, security, reliability, and misuse.
Assess generative-AI risks including hallucinations and harmful or sensitive outputs.
Classify AI use cases by impact, likelihood, and organizational risk appetite.
Define when enhanced review, testing, or human oversight is required.

Day 3 — Governance Structures, Policies and Controls

Design roles and decision rights across boards, executives, risk teams, and system owners.
Establish AI inventories, approval processes, documentation, and accountability.
Define policies for development, procurement, deployment, and acceptable use.
Build human oversight, escalation, and exception-management mechanisms.
Align governance with existing enterprise risk and compliance structures.

Day 4 — Assessing and Monitoring AI Systems

Define testing and evaluation criteria for performance and trustworthiness.
Examine fairness assessment, transparency, explainability, privacy, and security controls.
Evaluate vendors, foundation models, and other third-party AI dependencies.
Establish monitoring for drift, incidents, complaints, and changing use conditions.
Design evidence and reporting needed for internal assurance and leadership oversight.

Day 5 — Operationalizing Responsible AI

Apply governance decisions to realistic AI scenarios and ethical dilemmas.
Use the Govern, Map, Measure, and Manage logic to structure risk treatment.
Define incident response, remediation, and accountability when harm occurs.
Balance innovation speed with proportionate controls and stakeholder protection.
Produce a practical responsible-AI governance action plan for the organization.