A Conceptual Framework for Quality Assurance of LLM-based Socio-critical Systems

Recent breakthroughs in Artificial Intelligence (AI) obfuscate the boundaries between digital, physical, and social spaces, a trend expected to continue in the foreseeable future. Traditionally, software engineering has prioritized technical aspects, focusing on functional correctness and reliability while often neglecting broader societal implications. With the rise of software agents enabled by Large Language Models (LLMs) and capable of emulating human intelligence and perception, there is a growing recognition of the need for addressing socio-critical issues. Unlike technical challenges, these issues cannot be resolved through traditional, deterministic approaches due to their subjective nature and dependence on evolving factors such as culture and demographics. This paper dives into this problem and advocates the need for revising existing engineering principles and methodologies. We propose a conceptual framework for quality assurance where AI is not only the driver of socio-critical systems but also a fundamental tool in their engineering process. Such framework encapsulates pre-production and runtime workflows where LLM-based agents, so-called artificial doppelgängers, continuously assess and refine socio-critical systems ensuring their alignment with established societal standards.