Can you explain the different sources of bias in AI?

Feb 8, 2024 ... We developed a unified transformer-based machine learning framework ImmuBPI that comprised three tasks and achieved state-of-the-art performance. Mar 15, 2022 ... This document uses terms such as AI technology, AI system, and AI applications inter- changeably. Terms related to the machine learning pipeline ... Apr 18, 2017 ... Princeton University-based researchers have found that machine-learning programs can acquire the cultural biases embedded in the patterns of wording. Detecting bias in your ML model can be done proactively or reactively, and depending on your specific case, either one is viable. Reactive bias detection ... The bias–variance trade-off implies that a model should balance underfitting and overfitting: Rich enough to express underlying structure in data and simple ... We argue that algorithmic bias evolves with human interaction in an iterative manner, which may have a long-term effect on algorithm performance and humans' ... Aug 29, 2022 ... One of the main areas for concern is bias in AI systems. Bias can inappropriately skew the output from AI in favor of certain data sets; ... 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 ... 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 ... Bias in ML is an sort of mistake in which some aspects of a dataset are given more weight and/or representation than others. A skewed outcome, low accuracy ...