Understanding How Laws, Standards and Frameworks Apply to AI

How existing data privacy, IP, nondiscrimination, consumer protection and product liability laws apply to AI, combined with AI-specific laws and the main industry standards and tools.

5 minutes 5 Questions

Understanding how laws, standards, and frameworks apply to AI is a critical competency for AI Governance Professionals. This involves comprehending the complex regulatory landscape that governs the development, deployment, and use of artificial intelligence systems. **Laws** represent binding lega…

Concepts covered
Transparency, Choice and Lawful Basis Applied to AIPurpose Limitation Applied to AI ProcessingData Minimization and Privacy by Design for AIController Obligations Applied to AI: DPIAs and PIAsThird-Party Processors and Cross-Border Transfers for AIData Subject Rights Applied to AIAutomated Decision-Making Rules Under Privacy LawsIncident Management, Breach Notification and Record Keeping for AISensitive and Special Categories of Data in AI (Biometrics)Intellectual Property Laws Applied to AI Training DataNondiscrimination Laws Applied to AIConsumer Protection Laws Applied to AI (UDAP)Product Liability Laws Applied to AIEU AI Act Risk Classification FrameworkEU AI Act Requirements: Risk Management and Data GovernanceEU AI Act Requirements: Technical Documentation and Conformity AssessmentsEU AI Act Requirements: Human Oversight, Transparency and Quality ManagementGeneral-Purpose AI Model RequirementsAI Law Enforcement Framework and PenaltiesRoles Under AI Laws: Providers, Deployers, Importers and DistributorsSouth Korean AI Basic LawUS Federal and State AI Laws for Private SectorOECD AI Principles and FrameworkNIST AI Risk Management Framework Core FunctionsNIST AI RMF Playbook: Categories and SubcategoriesISO 22989 AI Concepts and TerminologyISO 42001 AI Management System StandardISO 42005 AI System Impact Assessment
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AIGP - Understanding How Laws, Standards and Frameworks Apply to AI Example Questions

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Question 1

In the NIST AI RMF Playbook, what are the four core functions that organize the framework's categories and subcategories?

Correct Answer: Govern, Map, Measure, and Manage

The NIST AI Risk Management Framework (AI RMF) is organized around four core functions: Govern, Map, Measure, and Manage, which provide a structured approach to addressing AI risks throughout the AI lifecycle.

One option lists functions from the NIST Cybersecurity Framework, not the AI RMF. Another option replaces Measure and Manage with Monitor and Mitigate, which are not the correct terms used in the framework. Another option substitutes Plan for Govern, missing the critical governance function that serves as the foundational and cross-cutting element across all other functions.

The key concept tested is knowledge of the NIST AI RMF's structural organization and its four core functions.

Question 2

A company is curating a dataset to train a text generation model. The team gathers thousands of articles licensed under Creative Commons Attribution-NonCommercial (CC BY-NC). They plan to sell subscription access to the final trained model to enterprise clients. How does the dataset's licensing condition relate to the team's planned commercialization strategy?

Correct Answer: The strategy conflicts with the restricted terms because selling subscription access constitutes commercialization of the underlying gathered training materials.

The correct answer is right because the Creative Commons Attribution-NonCommercial license explicitly prohibits using the licensed materials for commercial advantage or monetary compensation, meaning that utilizing these articles to train a commercialized AI model directly violates the terms.

The incorrect answers are wrong because charging enterprise subscription fees constitutes a commercial activity regardless of whether it supposedly covers infrastructure costs, transforming data into a model does not legally nullify the original licensing restrictions applied to the input dataset, and simply providing author attribution cannot override or waive the explicit non-commercial stipulation.

The key concept tested is intellectual property rights and dataset licensing constraints within AI governance.

Question 3

Which of the following is one of the five key principles outlined in the OECD AI Principles adopted in 2019?

Correct Answer: Transparency and explainability of AI systems

Transparency and explainability is one of the five key principles in the OECD AI Principles (also known as the OECD Recommendation on AI), which states that AI actors should commit to transparency and responsible disclosure so that people can understand AI-based outcomes and challenge them.

Mandatory open-source development is not an OECD requirement and would be overly restrictive to innovation. Full public disclosure of training data conflates transparency with an unrealistic and impractical mandate that ignores intellectual property and privacy concerns. Centralized government ownership of AI misrepresents accountability, which the OECD frames as responsibility of AI actors, not state control.

The key concept tested is familiarity with the OECD AI Principles framework and its five values-based principles for trustworthy AI.

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