All Episodes
Displaying 21 - 40 of 58 in total
Episode 21 — Operationalize AI Law Requirements for Risk Management, Documentation, and Record Keeping
This episode explains how legal requirements become real controls only when an organization turns them into repeatable operational practices. You will learn how risk m...
Episode 22 — Govern Human Oversight, Transparency, Notification, and Quality Management Requirements
This episode focuses on governance requirements that exist to keep AI systems understandable, reviewable, and controllable in real use. You will examine what meaningfu...
Episode 23 — Understand the Distinct Requirements That Apply to General-Purpose AI Models
This episode explains why general-purpose AI models can create governance challenges that differ from narrow, single-use systems. You will learn how models designed fo...
Episode 24 — Compare Enforcement, Penalties, and Duties for Providers, Deployers, Importers, and Distributors
This episode examines how governance obligations differ across entities that create, introduce, distribute, or use AI systems, and why those differences matter when le...
Episode 25 — Apply OECD Trustworthy AI Principles, Frameworks, Policies, and Recommended Practices
This episode introduces the practical value of broad AI principles and recommended practices by showing how they guide governance choices even when they are not writte...
Episode 26 — Use the NIST AI RMF and Playbook to Structure Governance
This episode explains how the NIST AI Risk Management Framework and its supporting playbook can help organizations turn broad governance goals into a structured operat...
Episode 27 — Understand ISO 22989, ISO 42001, and ISO 42005 in AI Governance
This episode introduces three ISO standards that matter because they help organizations describe AI consistently, build management systems, and guide governance practi...
Episode 28 — Review the Governance Foundations and Legal Duties Most Likely to Matter
This episode pulls together the major governance foundations and legal duties that repeatedly appear across AI oversight programs and exam scenarios. You will review w...
Episode 29 — Define Business Context and Use Cases Before Building Any AI System
This episode explains why good AI governance begins before model selection, procurement, or experimentation by forcing clarity about the business context and intended ...
Episode 30 — Perform Impact Assessments Early to Shape Safer AI Design Decisions
This episode focuses on impact assessments as early governance tools that shape design choices before risk becomes harder and more expensive to control. You will exami...
Episode 31 — Design AI Systems with Clear Purpose, Requirements, Architecture, and Model Choice
This episode explains how sound AI governance starts with disciplined design choices instead of jumping straight to tools or model hype. You will learn how to define t...
Episode 32 — Build Human Oversight, Metrics, Thresholds, Feedback, and Controls into Design
This episode focuses on designing governance into the system from the beginning by defining how people will supervise the AI, what measurements will show whether it is...
Episode 33 — Identify and Mitigate Design Risks with Harms Matrices, Risk Hierarchies, and Stakeholder Mapping
This episode explains how structured risk tools can improve design quality by forcing teams to think beyond technical accuracy and consider who could be affected, how ...
Episode 34 — Strengthen AI Designs Through Use-Case Evaluation, Benchmarking, Pilots, and Testing
This episode shows how design quality improves when organizations challenge assumptions before full deployment. You will examine how use-case evaluation helps confirm ...
Episode 35 — Document Design and Build Decisions to Prove Compliance and Manage Risk
This episode explains why documentation is not a bureaucratic afterthought but a core governance control that shows what was built, why it was built that way, and how ...
Episode 36 — Govern Training Data Rights, Quality, Quantity, Integrity, and Fitness for Purpose
This episode focuses on the governance questions surrounding training data, which often determine whether an AI system is lawful, reliable, and appropriate for its int...
Episode 37 — Establish Data Lineage and Provenance You Can Defend Under Scrutiny
This episode explains why organizations need to know where their data came from, how it moved, what changed along the way, and who handled it if they want defensible A...
Episode 38 — Plan Training and Testing Across Unit, Integration, Validation, Performance, Security, and Bias
This episode introduces a fuller view of AI assurance by showing how training and testing should span multiple layers rather than focusing on a single accuracy score. ...
Episode 39 — Improve Interpretability and Reduce Model Risk During AI Testing
This episode focuses on interpretability as a practical governance tool that helps organizations understand how a model behaves, where it is fragile, and how much trus...
Episode 40 — Manage Training and Testing Issues While Documenting Results for Compliance
This episode explains how organizations should handle problems discovered during training and testing without losing traceability or governance discipline. You will le...