What are the best practices for documenting and reporting on bias in AI research and development?

Jul 27, 2023 ... Bias is an additional parameter in a neural network that is used to adjust the output along with the weighted sum of the inputs to the neuron. Mar 12, 2023 ... Dec 11, 2018 ... As machine learning systems are used more and more to make decisions about insurance, criminal justice, credit, and child welfare, ... 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 ... Mar 3, 2021 ... Abstract. Western societies are marked by diverse and extensive biases and inequality that are unavoidably embedded in the data used to train ... Feb 4, 2019 ... Bias can creep in at many stages of the deep-learning process, and the standard practices in computer science aren't designed to detect it. Jun 1, 2023 ... In this review article, we discuss the implicit bias in training neural networks using gradient-based methods. Previous research revealed the existence of large near-surface temperature biases (mostly warm) over the Arctic sea ice in the current generation of atmospheric ... 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. Sep 1, 2022 ... Bias – A significant imbalance in the input data or model predictions across different facet values. What constitutes “significant” depends on ...