Bias and unfairness in machine learning models: a systematic review on datasets, tools, fairness metrics, and identification and mitigation methods

TP Pagano, RB Loureiro, FVN Lisboa… - Big data and cognitive …, 2023‏ - mdpi.com
One of the difficulties of artificial intelligence is to ensure that model decisions are fair and
free of bias. In research, datasets, metrics, techniques, and tools are applied to detect and …

A review on fairness in machine learning

D Pessach, E Shmueli - ACM Computing Surveys (CSUR), 2022‏ - dl.acm.org
An increasing number of decisions regarding the daily lives of human beings are being
controlled by artificial intelligence and machine learning (ML) algorithms in spheres ranging …

AI fairness in data management and analytics: A review on challenges, methodologies and applications

P Chen, L Wu, L Wang - Applied sciences, 2023‏ - mdpi.com
This article provides a comprehensive overview of the fairness issues in artificial intelligence
(AI) systems, delving into its background, definition, and development process. The article …

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

O Parraga, MD More, CM Oliveira, NS Gavenski… - ACM Computing …, 2023‏ - 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 …

Algorithmic fairness

D Pessach, E Shmueli - Machine Learning for Data Science Handbook …, 2023‏ - Springer
An increasing number of decisions regarding the daily lives of human beings are being
controlled by artificial intelligence (AI) and machine learning (ML) algorithms in spheres …

[HTML][HTML] Mitigating bias in artificial intelligence: Fair data generation via causal models for transparent and explainable decision-making

R González-Sendino, E Serrano, J Bajo - Future Generation Computer …, 2024‏ - Elsevier
In the evolving field of Artificial Intelligence, concerns have arisen about the opacity of
certain models and their potential biases. This study aims to improve fairness and …

The dark side of AI-enabled HRM on employees based on AI algorithmic features

Y Zhou, L Wang, W Chen - Journal of Organizational Change …, 2023‏ - emerald.com
Purpose AI is an emerging tool in HRM practices that has drawn increasing attention from
HRM researchers and HRM practitioners. While there is little doubt that AI-enabled HRM …

Balancing the scale: navigating ethical and practical challenges of artificial intelligence (AI) integration in legal practices

A Zafar - Discover Artificial Intelligence, 2024‏ - Springer
The paper explores the integration of artificial intelligence in legal practice, discussing the
ethical and practical issues that arise and how it affects customary legal procedures. It …

[HTML][HTML] Artificial intelligence potential for net zero sustainability: Current evidence and prospects

DB Olawade, OZ Wada, AC David-Olawade… - Next sustainability, 2024‏ - Elsevier
This comprehensive review explores the nexus between AI and the pursuit of net-zero
emissions, highlighting the potential of AI in driving sustainable development and combating …

Steering llms towards unbiased responses: A causality-guided debiasing framework

J Li, Z Tang, X Liu, P Spirtes, K Zhang, L Leqi… - arxiv preprint arxiv …, 2024‏ - arxiv.org
Large language models (LLMs) can easily generate biased and discriminative responses.
As LLMs tap into consequential decision-making (eg, hiring and healthcare), it is of crucial …