Machine Learning in Measurement Part 1: Error Contribution and Terminology Confusion

Like any science and engineering field, Instrumentation and Measurement (I&M) is currently experiencing the impact of the recent rise of Applied AI and in particular Machine Learning (ML) [1]. But I&M and ML use terminology that sometimes sound or look similar, though they might only have a marginal relationship or even be false friends. Therefore, understanding the terminology used by both communities and how they do and do not relate to one another is of crucial importance to understand the influences of ML in an I&M system. In addition, while I&M experts are well aware of the importance of measurement uncertainty, the concept has been understudied in the ML context. In this article, we will give an overview of ML's contribution to measurement error, and how to avoid confusion with the said terminology, to better understand the application of ML in measurement. Then, in Part 2 [2], we use that understanding and terminology to show how to quantify the uncertainty introduced by ML in a measurement system. This is of particular importance for measurement in the age of big data because we need to evaluate the trustworthiness of the available data and their impact on the derived conclusions and decision-making [3].