How can we ensure the robustness of fair AI models against adversarial attacks?

Oct 25, 2019 ... Machine learning systems disregard variables that do not accurately predict outcomes (in the data available to them). This is in contrast to ... Oct 8, 2021 ... Supervised machine learning depends on labelled datasets, often processed and prepared by a data scientist or specialist. Bias in this labelling ... May 1, 2022 ... It is the difference between the average estimator value (averaged over random samples) and the true value of the parameter. 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. This review will discuss the relevant ethical and bias considerations in AI-ML specifically within the pathology and medical domain. 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 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. Mar 3, 2021 ... In this paper we make three contributions. First, we assess the compatibility of fairness metrics used in machine learning against the aims and purpose of EU ... 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. 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.