Zobrazeno 1 - 10
of 485
pro vyhledávání: '"bias mitigation"'
Autor:
Dmytro Chornomordenko, Khalida Walid Nathim, Nada Abdulkareem Hameed, Saja Abdulfattah Salih, Nada Adnan Taher, Hayder Mahmood Salman
Publikováno v:
Proceedings of the XXth Conference of Open Innovations Association FRUCT, Vol 36, Iss 1, Pp 797-807 (2024)
The rapid integration of Artificial Intelligence (AI) into critical domains such as healthcare, finance, and criminal justice has raised significant ethical concerns, particularly around bias and fairness in machine learning models. Despite their pot
Externí odkaz:
https://doaj.org/article/1a7b7b682bf642c7bf157cd3fbc6ba59
Publikováno v:
BMC Genomics, Vol 25, Iss 1, Pp 1-19 (2024)
Abstract Background RNA sequencing is a vital technique for analyzing RNA behavior in cells, but it often suffers from various biases that distort the data. Traditional methods to address these biases are typically empirical and handle them individua
Externí odkaz:
https://doaj.org/article/4d06436d76624014a77129ac9675522a
A Multi-Objective Framework for Balancing Fairness and Accuracy in Debiasing Machine Learning Models
Publikováno v:
Machine Learning and Knowledge Extraction, Vol 6, Iss 3, Pp 2130-2148 (2024)
Machine learning algorithms significantly impact decision-making in high-stakes domains, necessitating a balance between fairness and accuracy. This study introduces an in-processing, multi-objective framework that leverages the Reject Option Classif
Externí odkaz:
https://doaj.org/article/09a4c6308b2340099b89bc9d1d2ec397
Publikováno v:
Computers and Education: Artificial Intelligence, Vol 7, Iss , Pp 100300- (2024)
Researchers have observed the relationship between educational achievements and students' demographic characteristics in physical classroom-based learning. In the context of online education, recent studies were conducted to explore the leading facto
Externí odkaz:
https://doaj.org/article/b3ff836b80c74f0b9a8f1408c1e82443
Publikováno v:
Computers and Education: Artificial Intelligence, Vol 7, Iss , Pp 100267- (2024)
This research investigates bias in AI algorithms used for monitoring student progress, specifically focusing on bias related to age, disability, and gender. The study is motivated by incidents such as the UK A-level grading controversy, which demonst
Externí odkaz:
https://doaj.org/article/78f706ccadda4872a2431ec4a53490b6
Autor:
Vien Ngoc Dang, Anna Cascarano, Rosa H. Mulder, Charlotte Cecil, Maria A. Zuluaga, Jerónimo Hernández-González, Karim Lekadir
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-12 (2024)
Abstract A significant level of stigma and inequality exists in mental healthcare, especially in under-served populations. Inequalities are reflected in the data collected for scientific purposes. When not properly accounted for, machine learning (ML
Externí odkaz:
https://doaj.org/article/4ca8b1d4989c428790c5207bbdf5a179
Publikováno v:
Machine Learning with Applications, Vol 19, Iss , Pp 100610- (2025)
Facial attribute classification algorithms frequently manifest demographic biases by obtaining differential performance across gender and racial groups. Existing bias mitigation techniques are mostly in-processing techniques, i.e., implemented during
Externí odkaz:
https://doaj.org/article/41f26f0d75e542309fc0052b29714827
Autor:
Melvin Kisten, Marshal Khosa
Publikováno v:
IEEE Access, Vol 12, Pp 177277-177284 (2024)
Credit assessment remains crucial in determining an individual’s creditworthiness, significantly influencing their financial opportunities. However, traditional credit assessment models have raised concerns about fairness and potential biases, whic
Externí odkaz:
https://doaj.org/article/337fc370f55043d98875a9216098b3af
Autor:
Jinkyu Lee, Jihie Kim
Publikováno v:
IEEE Access, Vol 12, Pp 161480-161489 (2024)
Understanding commonsense knowledge is crucial in the field of Natural Language Processing (NLP). However, the presence of demographic terms in commonsense knowledge poses a potential risk of compromising the performance of NLP models. This study aim
Externí odkaz:
https://doaj.org/article/b5f8d38d9a66465daee32f19d39b04bb
Publikováno v:
IEEE Open Journal of the Computer Society, Vol 5, Pp 406-417 (2024)
Detecting facial expressions is a challenging task in the field of computer vision. Several datasets and algorithms have been proposed over the past two decades; however, deploying them in real-world, in-the-wild scenarios hampers the overall perform
Externí odkaz:
https://doaj.org/article/ab7c4a5af4d9463d9dedf8216b2dbb6e