A 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.
An 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.
The 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.
The 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.
A 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.
A 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.
When an automated system treats people differently based on protected characteristics such as race, gender, age, or disability, resulting in unfair or unlawful outcomes.
The 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.
A mandatory conformity marking required under the EU AI Act for high-risk AI systems before they can be placed on the EU market, indicating that the system meets all applicable EU regulatory requirements including technical, safety, and fundamental rights obligations.
A formal process to verify that an AI system meets regulatory requirements before it is placed on the market or put into service. Required for high-risk AI systems under the EU AI Act.
Under the Colorado AI Act (as reenacted by SB 26-189), a decision that has a material, legal, or similarly significant effect on a consumer's access to or the cost of employment, education, housing, financial or lending services, insurance, health care, or essential government services. This is the trigger for the law's obligations when automated decision-making technology makes or materially influences the decision.
The ongoing process of collecting and analyzing data about an AI system's performance, behavior, and real-world impacts after deployment, to detect and address risks, drifts, biases, or failures that were not apparent during pre-deployment testing.
AI applications that are banned outright because they pose an unacceptable risk to fundamental rights, safety, or human dignity. The EU AI Act establishes the most comprehensive list of prohibited AI practices globally: eight prohibitions effective February 2, 2025, plus a ninth (non-consensual intimate imagery and CSAM generation) applying from December 2, 2026.
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