Audit
A systematic examination of an AI system's data, algorithms, processes, and outcomes to verify compliance, fairness, accuracy, and adherence to stated principles.
Why It Matters
AI audits provide accountability and transparency. They are increasingly required by regulation and demanded by stakeholders for high-stakes AI applications.
Example
An independent third party examining a bank's loan approval AI: reviewing training data for bias, testing outcomes across demographics, and verifying compliance with fair lending laws.
Think of it like...
Like a financial audit but for AI — independent experts examine the system to verify it is doing what it claims and meeting required standards.
Related Terms
AI Governance
The frameworks, policies, processes, and organizational structures that guide the responsible development, deployment, and monitoring of AI systems within organizations and across society.
Compliance
The process of ensuring AI systems meet regulatory requirements, industry standards, and organizational policies. AI compliance is becoming increasingly complex as regulations proliferate.
Fairness
The principle that AI systems should treat all individuals and groups equitably and not produce discriminatory outcomes. Multiple mathematical definitions of fairness exist, and they can sometimes conflict.
Bias in AI
Systematic errors in AI outputs that unfairly favor or disadvantage certain groups based on characteristics like race, gender, age, or socioeconomic status. Bias can originate from training data, model design, or deployment context.
Transparency
The principle that AI systems should operate in a way that allows stakeholders to understand how they work, what data they use, and how decisions are made.