Shifting paradigms in verification and validation of AI-enabled systems: A systems-theoretic perspective

Niloofar Shadab, Aditya U. Kulkarni, Alejandro Salado

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Scopus citations


There is a fundamental misalignment between current approaches to designing and executing verification and validation (V&V) strategies and the nature of AI-enabled systems. Current V&V approaches rely on the assumption that system behavior is preserved during a system's lifetime. However, AI-enabled systems are developed so that they evolve their own behavior during their lifetime; this is the consequence of learning by the AI-enabled system. This misalignment makes existing approaches to designing and executing V&V strategies ineffective. In this chapter, we will provide a systems-theoretic explanation for (1) why learning capabilities originate a unique and unprecedented family of systems, and (2) why current V&V methods and processes are not fit for purpose. AI-enabled systems necessitate a paradigm shift in V&V activities. To enable this shift, we will delineate a set of theoretical advances and process transformations that could support such shift.

Original languageEnglish (US)
Title of host publicationSystems Engineering and Artificial Intelligence
PublisherSpringer International Publishing
Number of pages16
ISBN (Electronic)9783030772833
ISBN (Print)9783030772826
StatePublished - Nov 2 2021


  • AI-enabled systems
  • Cyber-physical systems
  • Systems engineering
  • Systems theory
  • Verification and validation

ASJC Scopus subject areas

  • Computer Science(all)

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