AI Deployment Accountability Engineering: A Vision for Accountable AI in Safety-Critical Socio-Technical Systems

The increasing integration of Artificial Intelligence (AI) into safety-critical domains such as healthcare, finance, and public services necessitates a shift from purely model-centric evaluation to a more holistic approach. The proposed subdiscipline of AI Deployment Accountability Engineering (ADAE) aims to address the limitations of current pre-deployment assessments, which often fail to account for dynamic socio-technical environments, distribution shifts, institutional constraints, and human interaction, thus promoting accountable AI operations post-deployment.

This development highlights a critical gap in current AI governance and operational frameworks, emphasizing the need for comprehensive accountability throughout the AI lifecycle, especially in sensitive domains. Addressing this gap is crucial for maintaining public trust, mitigating risks, and ensuring the sustained, ethical, and effective deployment of AI technologies across various sectors.

AI systems are becoming critical in safety-critical sectors including healthcare, finance, and public services.

Forward consideration, not a verified fact.