How can we audit machine learning models for bias?

Previous research revealed the existence of large near-surface temperature biases (mostly warm) over the Arctic sea ice in the current generation of atmospheric ... The biases include those related to missing data and patients not identified by algorithms, sample size and underestimation, and misclassification and ... This manuscript is a literature study that provides a detailed survey regarding the different categories of bias and the corresponding approaches. This review will discuss the relevant ethical and bias considerations in AI-ML specifically within the pathology and medical domain. 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. Mar 10, 2025 ... PhyloFrame is an equitable machine learning method that creates signatures of disease based on population genomics data and disease-relevant ... Sep 11, 2024 ... Each chapter addresses a different topic and real-world case studies are featured throughout that highlight discriminatory machine learning ... Aug 8, 2023 ... Encord Active offers features to help reduce bias in datasets, allowing you to identify and address any potential biases in your data workflow. Oct 8, 2021 ... Supervised machine learning depends on labelled datasets, often processed and prepared by a data scientist or specialist. Bias in this labelling ... 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 ...