What are the ethical considerations related to biased AI in recruitment processes?

Aug 24, 2022 ... This report presents 12 suboptimal practices during data handling of an ML study, explains how those practices can lead to biases, and describes what may be ... 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 ... Apr 18, 2017 ... Princeton University-based researchers have found that machine-learning programs can acquire the cultural biases embedded in the patterns of wording. Mar 10, 2025 ... PhyloFrame is an equitable machine learning method that creates signatures of disease based on population genomics data and disease-relevant ... Feb 8, 2024 ... We developed a unified transformer-based machine learning framework ImmuBPI that comprised three tasks and achieved state-of-the-art performance. 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. This manuscript is a literature study that provides a detailed survey regarding the different categories of bias and the corresponding approaches. May 1, 2022 ... It is the difference between the average estimator value (averaged over random samples) and the true value of the parameter. Both of these existing methods address the problem of bias by adjusting the machine learning model. However, oftentimes it is the training data itself – i.e., ... Mar 5, 2019 ... Reports of successful applications of machine learning (ML) methods in structure-based virtual screening (SBVS) are increasing.