How can explainable AI (XAI) techniques help in understanding the sources and impacts of bias?

Aug 27, 2019 ... In this article, I'll dig into this question, its impact, and look at ways of eliminating bias from machine learning models. Mar 22, 2024 ... Research suggests that digital ageism, that is, age-related bias, is present in the development and deployment of machine learning (ML) models. Sep 1, 2022 ... Bias – A significant imbalance in the input data or model predictions across different facet values. What constitutes “significant” depends on ... 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 ... A method of assessing and correcting inaccuracies in a machine learning model. It involves measuring the difference between the model's predicted outcomes and ... 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. Jul 13, 2021 ... In this survey, we investigated different real-world applications that have shown biases in various ways, and we listed different sources of ... 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 ... Aug 22, 2024 ... Discover why bias and variance are two key components that you must consider when developing any good, accurate machine learning model. Apr 18, 2017 ... Princeton University-based researchers have found that machine-learning programs can acquire the cultural biases embedded in the patterns of wording.