Authors: Fuyuki Ishikawa
Tags: 2018, conceptual modeling
There have been active efforts to use machine learning (ML) techniques for the development of smart systems, e.g., driving support systems with image recognition. However, the behavior of ML components, e.g., neural networks, is inductively derived from training data and thus uncertain and imperfect. Quality assessment heavily depends on and is restricted by a test data set or what has been tried among an enormous number of possibilities. Given this unique nature, we propose a MLQ framework for assessing the quality of ML components and ML-based systems. We introduce concepts to capture activities and evidences for the assessment and support the construction of arguments.Read the full paper here: https://link-springer-com.proxy2.hec.ca/chapter/10.1007/978-3-030-00847-5_39
