When AI Procurement Fails, What Evidence Exists? (www.aivojournal.org)

Recent reports highlight significant failures in AI procurement where organizations relied on inaccurate AI-generated representations, leading to detrimental financial and reputational consequences. Acknowledging these issues, the focus shifts from merely assessing AI model accuracy to the evidentiary standards required after reliance has been established. Key questions arise regarding what information was presented, in what form, and whether that representation is preserved for external scrutiny. This shift emphasizes the importance of preserving AI-mediated outputs, as current practices often leave organizations without a reliable record of AI assertions at the moment decisions were made. With outputs generated dynamically and no immutable records maintained, firms may find it challenging to demonstrate accountability during audits or legal assessments. The ongoing scrutiny reveals a procedural control failure rather than a mere technical limitation, necessitating a robust governance framework for how AI-generated information is managed post-decision making. As the AI/ML community grapples with these challenges, the spotlight is on enhancing representation governance to ensure accountability and transparency in AI applications.