An overview of key trustworthiness attributes and KPIs for trusted ML-based systems engineering

Abstract

When deployed, machine-learning (ML) adoption depends on its ability to actually deliver the expected service safely, and to meet user expectations in terms of quality and continuity of service. For instance, the users expect that the technology will not do something it is not supposed to do, e.g., performing actions without informing users. Thus, the use of Artificial Intelligence (AI) in safety-critical systems such as in avionics, mobility, defense, and healthcare requires proving their trustworthiness through out its overall lifecycle (from design to deployment). Based on surveys on quality measures, characteristics and sub-characteristics of AI systems, the Confiance.ai program (www.confiance.ai) aims to identify the relevant trustworthiness attributes and their associated key performance indicators (KPI) or their associated methods for assessing the induced level of trust.