Can you discuss the bias-variance trade-off in relation to fairness?

Bias in deep learning: A case study · Missing Data and Patients Not Identified by Algorithms: They occur due to known or unknown gaps in datasets used to train ... Mar 1, 2021 ... "Bias" is used to characterize the model error due to simplified assumptions of the model, eg, using linear regression when linear regression is not complex ... Aug 13, 2018 ... This paper argues that algorithmic biases explicitly and implicitly harm racial groups and lead to forms of discrimination. Mar 20, 2023 ... Debiasing ML refers to the process of mitigating or removing the effects of bias from the model or the data used to train the model. Oct 16, 2023 ... AI bias, also referred to as machine learning bias or algorithm bias, refers to AI systems that produce biased results that reflect and ... 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 ... Tackling issues of bias and fairness when building and deploying machine learning and data science systems has received increased attention from the research ... Aug 12, 2020 ... What are Bias and Variance? · Bias: it's the difference between average predictions and true values. · Variance: it's the variability of our ... 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. In statistics and machine learning, the bias–variance tradeoff describes the relationship between a model's complexity, the accuracy of its predictions, ...