Measuring the Quality of Explanations: The System Causability Scale
  (SCS). Comparing Human and Machine Explanations
  Recent success in Artificial Intelligence (AI) and Machine Learning (ML)
allow problem solving automatically without any human intervention. Autonomous
approaches can be very convenient. However, in certain domains, e.g., in the
medical domain, it is necessary to enable a domain expert to understand, why an
algorithm came up with a certain result. Consequently, the field of Explainable
AI (xAI) rapidly gained interest worldwide in various domains, particularly in
medicine. Explainable AI studies transparency and traceability of opaque AI/ML
and there are already a huge variety of methods. For example with layer-wise
relevance propagation relevant parts of inputs to, and representations in, a
neural network which caused a result, can be highlighted. This is a first
important step to ensure that end users, e.g., medical professionals, assume
responsibility for decision making with AI/ML and of interest to professionals
and regulators. Interactive ML adds the component of human expertise to AI/ML
processes by enabling them to re-enact and retrace AI/ML results, e.g. let them
check it for plausibility. This requires new human-AI interfaces for
explainable AI. In order to build effective and efficient interactive human-AI
interfaces we have to deal with the question of how to evaluate the quality of
explanations given by an explainable AI system. In this paper we introduce our
System Causability Scale (SCS) to measure the quality of explanations. It is
based on our notion of Causability (Holzinger et al., 2019) combined with
concepts adapted from a widely accepted usability scale.
