What are some of the current research frontiers in the area of bias and fairness in machine learning?

Feb 8, 2024 ... We developed a unified transformer-based machine learning framework ImmuBPI that comprised three tasks and achieved state-of-the-art performance. Mar 10, 2025 ... PhyloFrame is an equitable machine learning method that creates signatures of disease based on population genomics data and disease-relevant ... Previous research revealed the existence of large near-surface temperature biases (mostly warm) over the Arctic sea ice in the current generation of atmospheric ... 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 15, 2022 ... This document uses terms such as AI technology, AI system, and AI applications inter- changeably. Terms related to the machine learning pipeline ... This review will discuss the relevant ethical and bias considerations in AI-ML specifically within the pathology and medical domain. Oct 14, 2020 ... The result of this technique is an RF bias-correction model that, when applied to SNODAS, offers improved daily estimates of SWE which can then ... 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 ... Feb 4, 2021 ... Data bias in machine learning is a type of error in which certain elements of a dataset are more heavily weighted and/or represented than others ... This manuscript is a literature study that provides a detailed survey regarding the different categories of bias and the corresponding approaches.