How can we address the underlying societal issues that contribute to bias in data and algorithms?

Feb 15, 2023 ... Conclusions. Our study illustrates the racial bias that can result when creating ML models without proper bias mitigation. Healthcare-related ML ... 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 ... 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. The bias–variance trade-off implies that a model should balance underfitting and overfitting: Rich enough to express underlying structure in data and simple ... Jun 3, 2014 ... We conduct a meta-analysis of all relevant, high quality primary studies of defect prediction to determine what factors influence predictive performance. 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 ... About the AMBP-L™ Certification System. IDC's Artificial & Machine Bias Prevention - Leader (AMBP-L)™ program enhances enterprise value by bridging the future ... 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. The biases include those related to missing data and patients not identified by algorithms, sample size and underestimation, and misclassification and ... Jul 15, 2024 ... As institutions of higher education turn to AI machine learning and data-driven algorithms to make their work more efficient, a new study ...