Algorithmic auditing is an instrument for dynamic appraisal and inspection of AI systems. This guide focuses on adversarial or third-party algorithmic audits, where independent auditors or communities thoroughly examine the functioning and impact of an algorithmic system, when access to the system is restricted. 

Adversarial audits bridge the gap between innovation potential and societal impact. 

The proposed approach is systematic and adaptable, incorporating qualitative contextual analysis, stakeholder mapping, evaluation of bias and inefficiencies, and reverse engineering of system processes through research. 

Adversarial audits become particularly significant when organizations are unwilling to undergo internal audits or when regulatory requirements for algorithmic audits are lacking.   

By serving as an independent mechanism to uncover potential negative impacts of algorithms and AI systems, these audits prompt developers to address issues and inform regulators and the public for appropriate actions. The ultimate goal is to ensure that AI systems operate with fairness and accountability, minimizing harmful consequences for society and protected groups. 

Read now the Adversarial Algorithmic Auditing Guide.