AI Compliance Glossary

Plain-language definitions of key AI compliance and regulatory terms, mapped to the laws that use them.

A B C E F G H I P R S T

A

Adverse ImpactA statistical finding that a selection process, test, or AI system produces substantially different pass rates, selection rates, or outcomes for different demographic groups, typically measured using the four-fifths (80%) rule from EEOC Uniform Guidelines.
NYC Local Law 144Colorado AI Act
AI DeployerAn organization or individual that integrates an AI system into its products, services, or internal processes for use in a specific application — as distinguished from the AI provider (developer) who built the underlying model or system.
EU AI ActColorado AI Act
AI GovernanceThe frameworks, policies, processes, and organizational structures that an organization establishes to ensure its AI systems are developed and used responsibly, in compliance with applicable laws, and aligned with ethical principles.
EU AI ActColorado AI Act
AI LiteracyThe knowledge, skills, and understanding needed to evaluate, use, and critically assess AI systems — including awareness of how AI works, its capabilities and limitations, and its potential social and ethical impacts. Required training for employees operating high-risk AI systems under the EU AI Act.
EU AI Act
AI Regulatory SandboxA controlled regulatory environment created by a government authority allowing AI developers — particularly startups and SMEs — to test innovative AI systems under real-world conditions with reduced regulatory burden and direct oversight, in exchange for transparency and data sharing with regulators.
EU AI Act
AI Risk Management SystemA documented, continuous process for identifying, analyzing, evaluating, and mitigating risks associated with an AI system throughout its entire lifecycle, from design through deployment and decommissioning.
EU AI ActColorado AI Act
Algorithmic DiscriminationWhen an automated system treats people differently based on protected characteristics — such as race, gender, age, or disability — resulting in unfair or unlawful outcomes.
Colorado AI ActNYC Local Law 144
Automated Decision-MakingThe use of computerized systems to make decisions about individuals with little or no human involvement, including decisions based on AI, machine learning, or rule-based algorithms that have legal or similarly significant effects.
EU AI ActColorado AI Act

B

Bias AuditAn impartial evaluation of an AI system's outputs to detect and measure discriminatory patterns across protected demographic groups. Required annually by NYC Local Law 144 for automated employment decision tools.
NYC Local Law 144Colorado AI Act

C

E

Explainability (AI)The ability to describe how an AI system reached a specific output or decision in terms that are understandable to a human audience — whether technical experts, affected individuals, or regulators. Required in various forms by the EU AI Act, GDPR, and US state AI laws.
EU AI ActColorado AI Act

F

Fundamental Rights Impact Assessment (FRIA)A structured evaluation required by the EU AI Act for public bodies and private entities deploying high-risk AI systems, assessing the potential impact on fundamental rights including privacy, non-discrimination, freedom of expression, and access to justice.
EU AI Act

G

General Purpose AI Model (GPAI)An AI model trained on large amounts of data at significant compute scale that can perform a wide range of distinct tasks, including text generation, translation, image creation, and code writing. Large language models like GPT-4 and Claude are leading examples.
EU AI ActCalifornia AB 2013

H

High-Risk AI SystemAn AI system that poses significant potential harm to individuals or society and is subject to heightened regulatory requirements. Different laws define this category differently.
Colorado AI ActEU AI Act

I

Impact AssessmentA structured evaluation of the potential harms, risks, and benefits of an AI system, required by several AI regulations before deploying high-risk systems.
Colorado AI ActEU AI Act

P

R

Red-Teaming (AI)A structured adversarial testing process where a dedicated team attempts to find ways to make an AI system behave harmfully, unsafely, or contrary to its intended design — including generating dangerous content, bypassing safety filters, or being manipulated into misuse.
EU AI Act

S

Systemic Risk (AI)Under the EU AI Act, systemic risk refers to risks posed by general-purpose AI models that, due to their capabilities, widespread deployment, or scale of training compute (above 10²⁵ FLOPs), have the potential to cause widespread negative effects across multiple sectors of society simultaneously.
EU AI Act

T

Training Data TransparencyThe practice of publicly disclosing information about the datasets used to train an AI system, including data sources, licensing status, known biases, and data governance practices. Required by California AB 2013 and the EU AI Act for covered AI developers.
California AB 2013EU AI Act

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