AI Governance: EU & US Regulations

Define the core ethical principles in AI: fairness, accountability, transparency, and explainability (FATE).

Analyze real-world examples of AI harms caused by bias, lack of transparency, or insufficient oversight.

Distinguish between aspirational ethical principles and legally binding AI regulations.

Evaluate an AI use case against responsible AI principles to identify potential ethical harms.

Describe the purpose, scope, and territorial reach of the EU AI Act.

Explain the EU AI Act's risk-based classification system (unacceptable, high, limited, minimal).

Classify a given AI use case into the appropriate EU AI Act risk category.

Identify AI practices that are prohibited under the EU AI Act.

Describe the transparency obligations for specific AI systems like chatbots and deepfakes.

List the main obligations for providers of high-risk AI systems (e.g., risk management, data governance).

Describe the key obligations for deployers (users) of high-risk AI systems.

Outline the conformity assessment process for high-risk AI systems.

Explain the requirements for technical documentation and record-keeping.

Identify the roles of key enforcement bodies like notified bodies and the EU AI Office.

Draft a high-level compliance checklist for a high-risk AI system under the EU AI Act.

Describe the US's sector-specific approach to AI regulation, contrasting it with the EU's horizontal approach.

Summarize the key directives of the White House Executive Order on AI.

Identify key US federal agencies and sector-specific guidance on AI (e.g., EEOC, FTC).

Explain the role and voluntary nature of the NIST AI Risk Management Framework (RMF) in the US context.

Describe the four core functions of the NIST AI RMF: Govern, Map, Measure, and Manage.

Apply the 'Govern' function to establish a risk management culture for a sample AI project.

Use the 'Map' function to identify the context and potential risks of a given AI system.

Use the 'Measure' function to analyze, assess, and track identified AI risks.

Use the 'Manage' function to prioritize and respond to a specific AI risk scenario.

Map the NIST AI RMF functions to the compliance requirements of the EU AI Act for high-risk systems.

Select an appropriate risk assessment methodology for a given AI system based on its context and potential impact.

Evaluate an AI system for risks of bias and fairness violations using common techniques.

Assess risks related to data quality and representativeness in AI training data.

Assess AI system risks related to data privacy, security, and consent management.

Document AI risk findings in a standardized risk register entry suitable for governance oversight.

Describe the purpose, key stages, and triggers for conducting an Algorithmic Impact Assessment (AIA).

Apply a structured methodology to scope an AIA for a given AI use case.

Synthesize findings from bias, privacy, and security risk assessments into a coherent AIA report.

Formulate mitigation strategies and recommendations based on the outcomes of an AIA.

Draft the charter for an AI governance program, defining its mission, scope, and objectives.

Outline the structure of a corporate AI policy framework, identifying key policies required (e.g., acceptable use, data handling).

Design an AI system inventory and risk classification process for an organization.

Outline a process for tracking and interpreting changes in the global AI regulatory landscape.

Design a procedure for ongoing AI model monitoring, including metrics for performance, data drift, and fairness.

Outline an audit plan to assess an AI system's compliance with either the EU AI Act or NIST AI RMF.

Identify and categorize types of AI-specific incidents (e.g., performance degradation, fairness violations).

Define an AI incident response lifecycle, including key stages, roles, and escalation pathways.

Articulate how existing IT governance experience translates directly to AI governance competencies.

Identify the key AI governance role types (e.g., AI Risk Manager, AI Auditor) and their responsibilities.

Research and describe the most in-demand AI governance skills and certifications in the current job market.

Draft a professional narrative for a resume or LinkedIn profile that connects regulatory knowledge with practical governance skills.