Regulatory Compliance & Legal Framework Flashcards
7 cards from real AML practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 7 Regulatory Compliance & Legal Framework flashcards as text
Which provision of GDPR specifically grants individuals the right to contest automated decisions that produce legal or similarly significant effects?
Answer: Article 22
GDPR Article 22 grants individuals the right not to be subject to solely automated decisions with significant effects and to request human review.
A hospital deploys an ML triage model. Which regulatory framework is most directly applicable in the US?
Answer: FDA Software as a Medical Device (SaMD) guidance
The FDA regulates ML-based software used in clinical decision-making as Software as a Medical Device (SaMD) under its digital health framework.
The concept of 'red-teaming' in the context of AI regulatory compliance refers to:
Answer: Adversarial testing to identify model failures, biases, and safety risks
Red-teaming in AI compliance involves adversarially probing models to uncover failure modes, harmful outputs, and systemic biases before and after deployment.
Section 5 of the FTC Act is relevant to ML deployments because it prohibits:
Answer: Unfair or deceptive acts or practices, including misleading algorithmic outputs
The FTC uses Section 5 authority to act against companies whose ML systems produce deceptive or unfair outcomes for consumers.
Which of the following is a core requirement of the Colorado AI Act (SB 21-169) for high-risk AI systems?
Answer: Conducting impact assessments and providing adverse action notices
Colorado's AI Act requires developers and deployers of high-risk AI to conduct impact assessments and provide consumers with adverse action notices and explanations.
In EU AI Act terminology, a 'general-purpose AI model' with systemic risk is characterized by training compute exceeding:
Answer: 10^25 FLOPs
The EU AI Act designates GPAI models trained with more than 10^25 FLOPs as having systemic risk, triggering additional obligations like adversarial testing.
A model card, as originally proposed by Mitchell et al. (2019), is best described as:
Answer: A structured transparency report disclosing model performance across subgroups and use contexts
Model cards are structured documentation tools that report a model's intended uses, performance across demographic subgroups, limitations, and ethical considerations.