Socio-Technical Auditing

Socio-technical auditing evaluates an AI system as a complete system, not just the AI models that are used. It assesses data flows, model behavior, business rules, user interface, human oversight, organizational processes, and the real-world outcomes the system produces. 

A purely technical evaluation can tell you how a model performs on a benchmark. A socio-technical evaluation tells you what happens when that model meets actual users, actual edge cases, and the human decisions that sit downstream of its outputs. 

This approach has been central to Eticas' work since 2012. Every evaluation we conduct follows a structured, end-to-end methodology organised across three stages in the system life cycle (Pre-Processing, In-Processing, and Post-Processing) and a set of core risk dimensions, including fairness, reliability, privacy, security, and governance. Our risk dimensions are refined and enriched as the technology and the regulatory landscape evolve.