Algorithmic Audits: A Practical Guide to Fairness, Transparency, and Accountability in AI
Algorithmic auditing is crucial for ensuring AI systems are fair, transparent, and accountable. A comprehensive audit should inspect the AI within its operational context, considering the data used and affected individuals. This approach applies to systems used for resource allocation, categorization, and identification in areas like healthcare and finance. Beyond bias, audits should assess social impact, user inclusion, and available recourse. The audit process involves creating model cards, mapping system interactions, identifying bias sources, and conducting bias testing, along with optional adversarial auditing for high-risk systems. Effective audit reports, including internal, public, and periodic versions, are vital for transparency and continuous improvement.
