What are some of the most pressing open research questions in this area?

Jan 21, 2021 ... Identifying Systemic Bias in the Acquisition of Machine Learning Decision Aids for Law Enforcement Applications ... Biased software tools that use ... Previous research revealed the existence of large near-surface temperature biases (mostly warm) over the Arctic sea ice in the current generation of atmospheric ... 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. 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, ... Mar 19, 2010 ... A bias value allows you to shift the activation function to the left or right, which may be critical for successful learning. Detecting bias in your ML model can be done proactively or reactively, and depending on your specific case, either one is viable. Reactive bias detection ... Dec 25, 2023 ... Machine learning bias, often referred to as algorithmic bias, is the tendency of a machine learning model to exhibit a preference or prejudice ... Dec 28, 2018 ... In this paper, we reconcile the classical understanding and the modern practice within a unified performance curve. This "double descent" curve ... Bias in ML is an sort of mistake in which some aspects of a dataset are given more weight and/or representation than others. A skewed outcome, low accuracy ... Feb 20, 2024 ... In this review, we present a framework for understanding the balance of opportunities and challenges for minimizing bias in medical imaging.