How can bias in word embeddings affect downstream NLP tasks?

This paper postulates that the root causes of bias are the prior decisions that affect- (a) what data was selected and (b) the labels assigned to those ... 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 ... 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. Oct 18, 2022 ... We develop and evaluate a machine learning system for identifying five major categories of biased and discriminatory language in job advertisements. Feb 7, 2022 ... We present a guide to the data ecosystem used by health insurers to highlight where bias can arise along machine learning pipelines. We suggest ... Apr 18, 2017 ... Princeton University-based researchers have found that machine-learning programs can acquire the cultural biases embedded in the patterns of wording. Jan 21, 2021 ... Identifying Systemic Bias in the Acquisition of Machine Learning Decision Aids for Law Enforcement Applications ... Biased software tools that use ... May 31, 2022 ... Additionally, ML algorithms tend to overfit to the data on which they are trained, which entails the learning of spurious relationships present ... We argue that algorithmic bias evolves with human interaction in an iterative manner, which may have a long-term effect on algorithm performance and humans' ...