AI systems make decisions affecting billions of people — from loan approvals to medical diagnoses to criminal sentencing. AI auditing ensures these systems work as intended and don't cause harm.
What Is an AI Audit?
An AI audit is a systematic evaluation of an AI system assessing:
- Fairness — does the system treat all groups equitably?
- Accuracy — does the system perform as claimed?
- Transparency — can decisions be explained and understood?
- Safety — does the system avoid harmful outputs?
- Privacy — does the system protect personal data?
- Compliance — does the system meet regulatory requirements?
Why Audit AI?
High-profile AI failures demonstrate the need:
- Amazon Hiring AI — automated resume screening that penalized women's applications
- COMPAS Recidivism — criminal justice risk scores with racial disparities
- Healthcare Algorithms — disease prediction that underserved Black patients
- Facial Recognition — error rates 10-100x higher for darker-skinned individuals
- Content Moderation — automated systems disproportionately censoring certain communities
Types of AI Audits
Different audits serve different purposes:
- Pre-Deployment Audit — evaluate before launching an AI system
- Continuous Monitoring — ongoing assessment of live systems
- Incident Investigation — deep dive after a problem is identified
- Compliance Audit — verify adherence to specific regulations
- Third-Party Audit — independent evaluation by external experts
The Growing Audit Market
AI auditing is becoming a professional discipline:
- Companies like ORCAA, Holistic AI, and Credo AI provide audit services
- Regulatory requirements (EU AI Act, NYC Local Law 144) mandate audits
- Professional certifications are emerging for AI auditors
- Insurance companies are requiring AI audits as part of underwriting