The purpose of the System Causability Scale is to provide a simple and rapid evaluation tool to measure the quality of an explanation interface (human-AI interface) or an explanation process itself. We were inspired by the System Usability Scale and the Framingham model which is often in use in daily routine. The limitations of the SCS is that Likert scales fall within the ordinal level of measurement, meaning that the response categories have a rank order. Despite this limits we are convinced that our Systems Causability Scale is useful for the international machine learning research community.
In the same way that usability encompasses measurements for the quality of use, causability encompasses measurements for the quality of explanations.
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