How can we develop more robust and generalizable methods for bias mitigation?

Mar 3, 2021 ... In this paper we make three contributions. First, we assess the compatibility of fairness metrics used in machine learning against the aims and purpose of EU ... 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. Oct 14, 2020 ... The result of this technique is an RF bias-correction model that, when applied to SNODAS, offers improved daily estimates of SWE which can then ... This review will discuss the relevant ethical and bias considerations in AI-ML specifically within the pathology and medical domain. 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. 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 ... Apr 27, 2023 ... Bias can be weeded out in a timely fashion if you collect reliable and comprehensive training data, select the most appropriate learning model, conduct ... This paper postulates that the root causes of bias are the prior decisions that affect- (a) what data was selected and (b) the labels assigned to those ... Oct 18, 2022 ... We develop and evaluate a machine learning system for identifying five major categories of biased and discriminatory language in job advertisements. Model bias, in Machine Learning, refers to the presence of systematic errors in a model that can cause it to consistently make incorrect predictions.