How can educational programs and training initiatives contribute to raising awareness about the issue of bias in AI and equipping professionals with the necessary skills to address it?

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 ... Jun 8, 2023 ... Bias mitigation in machine learning models · 1. Pre-processing algorithms · 2. In-processing algorithms · 3. Post-processing algorithms. The biases include those related to missing data and patients not identified by algorithms, sample size and underestimation, and misclassification and ... 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. Sep 5, 2022 ... This article aims to examine some of the ways in which racial bias gets included in AI and touch upon what can be done to mitigate these biases. Feb 7, 2022 ... We present a guide to the data ecosystem used by health insurers to highlight where bias can arise along machine learning pipelines. We suggest ... Mar 9, 2023 ... We know that synthetic data remains one of the most reliable and cost-effective solutions out there that improves the performance of the overall solution. 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 ... Sep 11, 2024 ... Each chapter addresses a different topic and real-world case studies are featured throughout that highlight discriminatory machine learning ... This review will discuss the relevant ethical and bias considerations in AI-ML specifically within the pathology and medical domain.