AI-AUDIT-FRAMEWORK

AI Audit Framework AI safety + governance framework.

Definition

AI Audit Framework refers to emerging methodologies + frameworks for structured AI system auditing focusing on fairness + bias + privacy + cybersecurity + explainability + multiple AI quality dimensions, examples include ICO UK Auditing AI Framework 2020 + IIA Internal Audit AI Framework 2017 + multiple private firms methodologies (Deloitte + KPMG + EY + PwC AI audit practices), foundational for emerging EU AI Act conformity assessments by Notified Bodies + internal audit functions + third-party AI audit services market growing 2020s+. Framework + standard + guidance objectives AI safety + governance + risk management + transparency + fairness + accountability + explainability + multiple AI ethics principles adoption multi-stakeholder process + voluntary adoption industry + emerging regulation alignment EU AI Act + US Executive Orders + multiple national jurisdictions + private sector adoption Microsoft + Google + Meta + OpenAI + Anthropic + other major AI labs + corporate compliance teams 2020s+.

Origin

AI audit frameworks emergence 2017+ ; ICO UK Auditing AI Framework 2020 ; IIA Internal Audit AI Framework 2017 ; EU AI Act Notified Body assessments 2027+.

Example in context

European insurance company commissions third-party AI audit of its underwriting AI model: auditor (e.g., Deloitte AI audit practice) uses comprehensive AI audit framework evaluating model design + data + training + deployment + monitoring across fairness + bias + privacy + cybersecurity + explainability + business performance dimensions, identifies areas for improvement + documents conformity to EU AI Act + ISO/IEC 23894 + multiple standards, audit report supports regulatory submissions + governance.

Related terms

• Algorithmic Impact Assessment — complementary AI assessment.