What are the potential ramifications of bias in financial applications of machine learning, such as credit scoring or loan approval?

Mar 10, 2025 ... PhyloFrame is an equitable machine learning method that creates signatures of disease based on population genomics data and disease-relevant ... As promising as machine-learning technology is, it can also be susceptible to unintended biases that require careful planning to avoid. Jul 3, 2021 ... Weights and biases (commonly referred to as w and b) are the learnable parameters of a some machine learning models, including neural networks. May 1, 2022 ... It is the difference between the average estimator value (averaged over random samples) and the true value of the parameter. Tackling issues of bias and fairness when building and deploying machine learning and data science systems has received increased attention from the research ... 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 9, 2023 ... We know that synthetic data remains one of the most reliable and cost-effective solutions out there that improves the performance of the overall solution. Dec 25, 2023 ... Machine learning bias, often referred to as algorithmic bias, is the tendency of a machine learning model to exhibit a preference or prejudice ... 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 18, 2022 ... We develop and evaluate a machine learning system for identifying five major categories of biased and discriminatory language in job advertisements.