What are some emerging research directions in the field of fair machine learning?

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 linear regression analysis, bias refers to the error that is introduced by approximating a real-life problem, which may be complicated, by a much simpler ... A method of assessing and correcting inaccuracies in a machine learning model. It involves measuring the difference between the model's predicted outcomes and ... 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. 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. Jun 8, 2023 ... Bias mitigation in machine learning models · 1. Pre-processing algorithms · 2. In-processing algorithms · 3. Post-processing algorithms. Feb 21, 2022 ... A model's ability to generalize is influenced by both the diversity of the data and the way the model is trained, researchers report. 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 ... 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 ... Jul 19, 2021 ... ... bias in artificial intelligence was identified and the ethical risk mitigated. ... We can build a machine-learning model that works well at ...