CAIC AI Governance & Compliance Flashcards
6 cards from real CAIC practice questions. Tap to flip, then mark Knew It or Still Learning โ missed cards come back until you master them.
Read the first 6 CAIC AI Governance & Compliance flashcards as text
Which US federal framework provides guidelines for managing AI risks in organizations?
Answer: NIST AI Risk Management Framework (AI RMF)
The NIST AI RMF provides voluntary guidance to help organizations identify, assess, and manage AI risks across the AI system lifecycle.
What is 'algorithmic accountability' in AI governance?
Answer: The obligation to explain, audit, and take responsibility for automated decision outcomes
Algorithmic accountability requires organizations to be able to explain how automated decisions are made and to take responsibility when those decisions cause harm.
Under the EU AI Act, which category describes AI systems used in critical infrastructure that pose significant risk?
Answer: High risk
The EU AI Act classifies AI used in critical infrastructure, employment, education, and essential services as 'high risk,' subject to strict conformity requirements.
What is the purpose of an AI model card in responsible AI deployment?
Answer: To document a model's intended use, performance metrics, limitations, and ethical considerations
Model cards are structured documents that disclose key information about an AI model, including its training data, evaluation results, intended use cases, and known limitations.
Which principle of responsible AI ensures that AI system decisions can be understood and traced by humans?
Answer: Explainability
Explainability (also called interpretability) ensures that AI decisions and the reasoning behind them can be understood by developers, auditors, and affected individuals.
A CAIC consultant discovers an AI hiring tool disproportionately rejects candidates from certain demographic groups. This is an example of:
Answer: Algorithmic bias
Algorithmic bias occurs when an AI system produces systematically unfair outcomes for specific groups, often due to biased training data or flawed model design.