The 'Changing Anything Changes Everything (CACE)' Principle: Underestimated Challenges in Applying AI to Automotive Safety-Critical Systems

The integration of artificial intelligence (AI) into automotive safety-critical systems presents unique challenges among which the "changing anything changes everything" property inherent in many AI models often overlooked or underestimated. This principle underscores the high degree of interdependence within AI systems, where even minor adjustments can have significant, widespread, unforeseen impacts on system behavior. While the automotive industry is increasingly adopting AI to various vehicle applications and features, the cascading effects of AI's CACE property can lead to significant risks in safety-critical applications. This paper explores the intricate nature of AI CACE principle and how this principle will impact the development cycle of AI based applications, emphasizing how minor updates or changes can propagate through the system, altering performance and potentially compromising safety. By examining case studies and theoretical analysis, we highlight the hidden dangers of this interconnectivity and discuss potential strategies to mitigate the risks associated with deploying AI in environments where safety is paramount. Our analysis aims to bring attention to this overlooked challenge, offering insights into more safe, robust and effective AI deployment practices in the automotive industry.