A Conceptual Model of Operationalizable Trustworthiness for Autonomous Systems

Autonomous systems are increasingly deployed across critical domains, yet research on trustworthy autonomous
systems (TAS) appears to lack an agreed-upon, operationalizable taxonomy for trustworthiness characteristics. While trustworthy artificial intelligence (TAI) frameworks offer measurable characteristics with more established metrics, TAS literature tends to be fragmented with high-level models that can be difficult to apply in practice. This paper investigates the extent of alignment in TAS trustworthiness research and identifies potential sources of misalignment, including differences in system definitions, autonomy properties, stakeholder perspectives, and domain requirements. Based on this analysis, we synthesize a foundational two-pillar core of trustworthiness, the system’s ability to dependably do what it is intended to do and the extent to which stakeholders can assess and justify confidence in this behavior. We propose an initial operationalization of this core using characteristics adapted from established TAI frameworks for which metrics and evaluation methods exist, and outline how it can be applied to specific application contexts. ... mehrAn example shows how stakeholder analysis guides characteristic adaptation.