What are the challenges in addressing intersectional bias in machine learning?

Model bias, in Machine Learning, refers to the presence of systematic errors in a model that can cause it to consistently make incorrect predictions. This review will discuss the relevant ethical and bias considerations in AI-ML specifically within the pathology and medical domain. Mar 26, 2018 ... In the case of self-learning systems, the type of “garbage” is biased data. Left unchecked, feeding biased data to self-learning systems can ... Oct 25, 2019 ... Machine learning systems disregard variables that do not accurately predict outcomes (in the data available to them). This is in contrast to ... Feb 21, 2022 ... A model's ability to generalize is influenced by both the diversity of the data and the way the model is trained, researchers report. 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 ... 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 ... Aug 18, 2021 ... We demonstrate that state-of-the-art scheduling systems cause the black patients in our data set to wait about 30% longer than nonblack patients. Defining data bias and the list of common types of data bias in ML: selection bias, overfitting/underfitting, outliers, measurement bias, recall bias, ... May 23, 2016 ... Machine Bias. There's ... The advantage of using a generic statistical learning algorithm is that you KNOW what information goes in.