How can bias affect the use of machine learning in human resources and hiring processes, such as resume screening?

Feb 7, 2024 ... The landscape is overrun with “neutral” analysts using some kind of sentiment analysis to lazily label something as right or left. This MIT ... 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 ... 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. 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 ... The bias–variance trade-off implies that a model should balance underfitting and overfitting: Rich enough to express underlying structure in data and simple ... 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 ... 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. Apr 18, 2017 ... Princeton University-based researchers have found that machine-learning programs can acquire the cultural biases embedded in the patterns of wording. Detecting bias in your ML model can be done proactively or reactively, and depending on your specific case, either one is viable. Reactive bias detection ... Aug 14, 2018 ... This article is based on Rachel Thomas's keynote presentation, “Analyzing & Preventing Unconscious Bias in Machine Learning” at QCon.ai 2018.