How can we ensure that automated fairness tools are themselves unbiased and reliable?

This manuscript is a literature study that provides a detailed survey regarding the different categories of bias and the corresponding approaches. Mar 15, 2022 ... In this article, we will cover bias concerning ML modeling, types of biases involved in developing machine learning models, methods to detect biases, and their ... Jul 12, 2023 ... Healthcare algorithms and AI bias can contribute to existing health disparities for certain populations based on race, ethnicity, gender, age, or other ... Apr 18, 2017 ... Princeton University-based researchers have found that machine-learning programs can acquire the cultural biases embedded in the patterns of wording. Aug 22, 2024 ... Discover why bias and variance are two key components that you must consider when developing any good, accurate machine learning model. Feb 8, 2024 ... We developed a unified transformer-based machine learning framework ImmuBPI that comprised three tasks and achieved state-of-the-art performance. Jul 25, 2022 ... Machine learning bias is sometimes called algorithm bias or AI bias. Predictive models may be trained on data containing human decisions or ... This review will discuss the relevant ethical and bias considerations in AI-ML specifically within the pathology and medical domain. 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. 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 ...