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Key topics include:
• AI Risk Identification & Mitigation
Learn to detect, assess, and resolve AI risks during design and development.
• Data Governance, Fairness & Bias Handling
Apply ethical data practices and reduce bias using reproducible technical methods.
• Technical Documentation for Transparency
Create structured technical documentation that builds trust and supports internal or regulatory review.
• Monitoring AI for Safety & Accuracy
Set up continuous performance checks, escalation paths, and system monitoring that scale.
• Understanding AI Policy Trends
Gain a working knowledge of international regulations (like the EU AI Act) and how they influence development strategy — no legal background required.
• Post-Deployment Monitoring Plans
Develop structured workflows to track model drift, feedback, and real-world outcomes post-launch.
• Incident Reporting & Issue Escalation
Establish processes to recognize, log, and escalate significant AI-related incidents across teams.
• Managing Vendor & Third-Party AI Risk
Identify and evaluate external AI tools and APIs with confidence, and ask the right compliance questions.
• Designing with Governance in Mind
Use governance-informed design principles to make auditability and oversight a feature, not a bolt-on.
• Bias Detection & Mitigation in Dev Workflows
Integrate fairness metrics and mitigation strategies into your training, validation, and deployment pipelines.
• Secure Deployment & Model Robustness
Protect your AI systems from security threats, data poisoning, and performance degradation over time.
• Lifecycle Management & Versioning
Maintain detailed change logs, rollback protocols, and version control from training to production.
• Developer-Focused Documentation Practices
Provide critical technical assets — like logs, model cards, and changelogs — in formats compliance teams can use.
• Human Oversight in Practice
Build and support Human-in-the-Loop and Human-on-the-Loop systems with real accountability baked into code.
Ideal for AI developers, IT professionals, data scientists, and ML engineers working in product teams, R&D, or technical compliance — especially in organizations deploying AI globally.
