How can we monitor deployed machine learning models for bias drift?

This manuscript is a literature study that provides a detailed survey regarding the different categories of bias and the corresponding approaches. Jul 19, 2021 ... ... bias in artificial intelligence was identified and the ethical risk mitigated. ... We can build a machine-learning model that works well at ... Aug 22, 2024 ... Discover why bias and variance are two key components that you must consider when developing any good, accurate machine learning model. Apr 27, 2023 ... Bias can be weeded out in a timely fashion if you collect reliable and comprehensive training data, select the most appropriate learning model, conduct ... Aug 8, 2024 ... We show that the ML model can effectively reduce climate bias in large-scale wind, temperature, and humidity while preserving their responses to imposed ... Aug 18, 2021 ... We demonstrate that state-of-the-art scheduling systems cause the black patients in our data set to wait about 30% longer than nonblack patients. Feb 4, 2021 ... Data bias in machine learning is a type of error in which certain elements of a dataset are more heavily weighted and/or represented than others ... Bias in deep learning: A case study · Missing Data and Patients Not Identified by Algorithms: They occur due to known or unknown gaps in datasets used to train ... Jun 3, 2014 ... We conduct a meta-analysis of all relevant, high quality primary studies of defect prediction to determine what factors influence predictive performance. Jul 28, 2022 ... Long Story Short: Omitted Variable Bias in Causal Machine Learning ... We derive general, yet simple, sharp bounds on the size of the omitted ...