What are the best practices for documenting and reporting on bias in machine learning projects?

Sep 1, 2022 ... Bias – A significant imbalance in the input data or model predictions across different facet values. What constitutes “significant” depends on ... Previous research revealed the existence of large near-surface temperature biases (mostly warm) over the Arctic sea ice in the current generation of atmospheric ... Mar 20, 2023 ... Debiasing ML refers to the process of mitigating or removing the effects of bias from the model or the data used to train the model. May 1, 2022 ... It is the difference between the average estimator value (averaged over random samples) and the true value of the parameter. Nov 10, 2017 ... Machine learning can perpetuate and even amplify behavioral biases. By design, a social-media site filtering news based on user preferences ... 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 ... As promising as machine-learning technology is, it can also be susceptible to unintended biases that require careful planning to avoid. Jul 24, 2024 ... Introduction: Machine learning (ML) algorithms have been heralded as promising solutions to the realization of assistive systems in digital ... Mar 10, 2025 ... PhyloFrame is an equitable machine learning method that creates signatures of disease based on population genomics data and disease-relevant ... Aug 18, 2023 ... Here, we apply machine learning (ML) to produce a blended TROPOMI+GOSAT product that uses GOSAT to correct biases in the TROPOMI data and ...