How can biased credit scoring models perpetuate existing patterns of financial inequality?

Sep 3, 2024 ... AI bias occurs when an AI system produces systematically prejudiced results due to erroneous assumptions in the machine learning process. These ... Nov 11, 2022 ... Phoebe sent me a letter. She wants to build a Machine Learning model using a small dataset. Should she try a model with low or high variance ... Feb 8, 2024 ... We developed a unified transformer-based machine learning framework ImmuBPI that comprised three tasks and achieved state-of-the-art performance. The purpose of this study is to illustrate the harmful effects of learned human-like biases in MLAGs, to highlight how improper algorithm training leads to ... Apr 3, 2025 ... Bias Variance Tradeoff. If the algorithm is too simple (hypothesis with linear equation) then it may be on high bias and low variance condition ... This manuscript is a literature study that provides a detailed survey regarding the different categories of bias and the corresponding approaches. Mar 10, 2025 ... PhyloFrame is an equitable machine learning method that creates signatures of disease based on population genomics data and disease-relevant ... Feb 21, 2024 ... USC computer science researchers propose a novel approach to mitigate bias in machine learning model training, specifically in image generation. Aug 26, 2022 ... The test provides a strict control for type I errors and high statistical power, even for nonnormally and nonlinearly dependent predictions, often seen in ... May 1, 2022 ... It is the difference between the average estimator value (averaged over random samples) and the true value of the parameter.