An algorithm audit strives to diagnose unwanted consequences. An audit represents a complex set of techniques to identify flaws in the system. In an algorithm audit, AI is being reviewed for various scenarios to pinpoint bias or blind spots.
There are two types of algorithmic audit: direct and indirect. Direct audit represents a thorough review of the code where clear decision trees and regression models can be traced and evaluated. This is the rather traditional approach and has limitations, as can be applied only to parts of the easily understood algorithms and the impact isolated. An indirect audit consists of feeding test data sets to AI and examining the outcomes to identify bias or unwanted results. This method is especially vital for deep learning systems, where the algorithms are being developed autonomously.
By nature, audits work best if done regularly. The same rule applies to algorithm audits – as AI systems evolve, unlawful discrimination, new biases, or blind spots may emerge. Having a continuous process of probing AI elements will bring the best results and ensure limited unwanted outcomes