[HTML][HTML] Fairness, Accountability, Transparency, and Ethics (FATE) in Artificial Intelligence (AI) and higher education: A systematic review

B Memarian, T Doleck - Computers and Education: Artificial Intelligence, 2023‏ - Elsevier
Abstract Background The use of Artificial Intelligence or AI is rising in higher education. With
this rise, the morality of AI programs is being questioned. There is, as such, a need to …

Bias mitigation for machine learning classifiers: A comprehensive survey

M Hort, Z Chen, JM Zhang, M Harman… - ACM Journal on …, 2024‏ - dl.acm.org
This article provides a comprehensive survey of bias mitigation methods for achieving
fairness in Machine Learning (ML) models. We collect a total of 341 publications concerning …

Fairness in deep learning: A survey on vision and language research

O Parraga, MD More, CM Oliveira, NS Gavenski… - ACM Computing …, 2025‏ - dl.acm.org
Despite being responsible for state-of-the-art results in several computer vision and natural
language processing tasks, neural networks have faced harsh criticism due to some of their …

[PDF][PDF] Towards guaranteed safe ai: A framework for ensuring robust and reliable ai systems

D Dalrymple, J Skalse, Y Bengio… - arxiv preprint arxiv …, 2024‏ - eecs.berkeley.edu
Ensuring that AI systems reliably and robustly avoid harmful or dangerous behaviours is a
crucial challenge, especially for AI systems with a high degree of autonomy and general …

An evaluation of synthetic data augmentation for mitigating covariate bias in health data

L Juwara, A El-Hussuna, K El Emam - Patterns, 2024‏ - cell.com
Data bias is a major concern in biomedical research, especially when evaluating large-scale
observational datasets. It leads to imprecise predictions and inconsistent estimates in …

Machine vision combined with deep learning–based approaches for food authentication: An integrative review and new insights

C Shen, R Wang, H Nawazish, B Wang… - … Reviews in Food …, 2024‏ - Wiley Online Library
Food fraud undermines consumer trust, creates economic risk, and jeopardizes human
health. Therefore, it is essential to develop efficient technologies for rapid and reliable …

Multi-task learning with dynamic re-weighting to achieve fairness in healthcare predictive modeling

C Li, S Ding, N Zou, X Hu, X Jiang, K Zhang - Journal of biomedical …, 2023‏ - Elsevier
The emphasis on fairness in predictive healthcare modeling has increased in popularity as
an approach for overcoming biases in automated decision-making systems. The aim is to …

Debiasing methods for fairer neural models in vision and language research: A survey

O Parraga, MD More, CM Oliveira, NS Gavenski… - arxiv preprint arxiv …, 2022‏ - arxiv.org
Despite being responsible for state-of-the-art results in several computer vision and natural
language processing tasks, neural networks have faced harsh criticism due to some of their …

Addressing bias in bagging and boosting regression models

J Ugirumurera, EA Bensen, J Severino, J Sanyal - Scientific Reports, 2024‏ - nature.com
As artificial intelligence (AI) becomes widespread, there is increasing attention on
investigating bias in machine learning (ML) models. Previous research concentrated on …

FATE in MMLA: A Student-Centred Exploration of Fairness, Accountability, Transparency, and Ethics in Multimodal Learning Analytics

Y **, V Echeverria, L Yan, L Zhao, R Alfredo… - arxiv preprint arxiv …, 2024‏ - arxiv.org
Multimodal Learning Analytics (MMLA) integrates novel sensing technologies and artificial
intelligence algorithms, providing opportunities to enhance student reflection during …