Results on differential and dependent measurement error of the exposure and the outcome using signed directed acyclic graphs.

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Prediction-Based Decisions and Fairness: A Catalogue of Choices, Assumptions, and Definitions (2018, background): “Similarly systematic measurement error, particularly when the error is greater for some groups than others (known as differential measurement error [123]), can have profound consequences.”
A Framework for Understanding Unintended Consequences of Machine Learning (2019, background): “On the other hand, measurement bias often arises because proxies are generated differently across groups (also known as differential measurement error (VanderWeele and Hernán, 2012)).”