Methodology to characterize and assess Trust for AI-based safety critical system

The assessment of AI-based systems trustworthiness is a challenging process given the complexity of the subject which involves qualitative and quantifiable concepts, a wide heterogeneity and granularity of attributes, and in some cases even the non-commensurability of the latter. Evaluating trustworthiness of AI-enabled systems is in particular decisive in safety-critical domains where AIs are expected to mostly operate autonomously. To overcome these issues, we proposes an innovative solution based upon a multi-criteria decision analysis. The approach encompasses several phases: structuring trustworthiness as a set of well-defined attributes, the exploration of attributes to determine related performance metrics (or indicators), the selection of assessment methods or control points, and structuring a multi-criteria aggregation method to estimate a global evaluation of trust.