What are some strategies for building fair and equitable AI systems for healthcare?

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' ... Oct 9, 2024 ... Selection bias occurs if a dataset's examples are chosen in a way that is not reflective of their real-world distribution. Selection bias can ... Jun 8, 2023 ... Bias mitigation in machine learning models · 1. Pre-processing algorithms · 2. In-processing algorithms · 3. Post-processing algorithms. Oct 8, 2021 ... Supervised machine learning depends on labelled datasets, often processed and prepared by a data scientist or specialist. Bias in this labelling ... Jul 28, 2022 ... Long Story Short: Omitted Variable Bias in Causal Machine Learning ... We derive general, yet simple, sharp bounds on the size of the omitted ... Mar 1, 2021 ... "Bias" is used to characterize the model error due to simplified assumptions of the model, eg, using linear regression when linear regression is not complex ... Bias in deep learning: A case study · Missing Data and Patients Not Identified by Algorithms: They occur due to known or unknown gaps in datasets used to train ... 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 ... Sep 15, 2023 ... In this article, we will embark on a journey to understand bias and fairness in machine learning algorithms, exploring their implications, real-world examples, ... Previous research revealed the existence of large near-surface temperature biases (mostly warm) over the Arctic sea ice in the current generation of atmospheric ...