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, the Confiance.ai program [1] 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. The approach is illustrated by applying some performance metrics to a data-driven AI context whereas the focus on aggregation methods is left as a near-term perspective of Confiance.ai milestones.