How can we incorporate fairness constraints directly into machine learning algorithms?

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' ... The bias–variance trade-off implies that a model should balance underfitting and overfitting: Rich enough to express underlying structure in data and simple ... Defining data bias and the list of common types of data bias in ML: selection bias, overfitting/underfitting, outliers, measurement bias, recall bias, ... 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 ... Feb 21, 2024 ... USC computer science researchers propose a novel approach to mitigate bias in machine learning model training, specifically in image generation. Jun 8, 2023 ... Bias mitigation in machine learning models · 1. Pre-processing algorithms · 2. In-processing algorithms · 3. Post-processing algorithms. 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. Jun 1, 2023 ... In this review article, we discuss the implicit bias in training neural networks using gradient-based methods. Tackling issues of bias and fairness when building and deploying machine learning and data science systems has received increased attention from the research ... Sep 1, 2022 ... Bias – A significant imbalance in the input data or model predictions across different facet values. What constitutes “significant” depends on ...