AI-assisted decision-making that impacts individuals raises critical questions about transparency and fairness in artificial intelligence (AI). Much research has highlighted the reciprocal relationships between the transparency/explanation and fairne...
In this study, we analyze “Discrimination”, ”Bias”, “Fairness”, and “Trustworthiness” as working variables in the context of the social impact of AI. It has been identified that there exists a set of sp...
We propose definitions of fairness in machine learning and artificial intelligence systems that are informed by the framework of intersectionality, a critical lens from the legal, social science, and humanities literature which analyzes how interlock...
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 explores the fairness problem in AI through practical applica...
Participatory design (PD) and co-creation (Co-C) approaches to building Artificial Intelligence (AI) systems have become increasingly popular exercises for ensuring greater social inclusion and fairness in technological transformation by accounting f...
This study investigates the impact of artificial intelligence (AI) on financial inclusion satisfaction and recommendation, with a focus on the ethical dimensions and perceived algorithmic fairness. Drawing upon organizational justice theory and the h...
The integration of artificial intelligence (AI) in the financial sector has seen a rapid increase over the past few years, offering new possibilities to streamline processes while ensuring profitability for lending institutions. With its data-driven...
In the evolving digital landscape, the pervasive influence of artificial intelligence (AI) on social media platforms reveals a compelling paradox: the capability to provide personalized experiences juxtaposed with inherent biases reminiscent of human...
As artificial intelligence systems increasingly govern public safety operations, concerns over algorithmic fairness and legal compliance intensify. This study introduces a scenario-based evaluation framework (SCRAM) that simultaneously measures regul...
Artificial intelligence (AI) is increasingly utilized in higher-education assessment; however, existing research remains fragmented, with limited synthesis regarding the interplay of fairness, transparency, pedagogy, and governance. To address this g...
Depression poses a major worldwide health burden, necessitating advanced diagnostic tools based on reliable and authentic assessment. While multimodal AI systems offer promising results by integrating diverse data sources, existing approaches often r...
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This study examines the role of algorithmic fairness in alleviating digital financial stress among consumers across OECD countries, utilizing panel data spanning from 2010 to 2023. By introducing a digital financial stress index—constructed fro...
Autism spectrum disorder (ASD) has been associated with conditions like depression, anxiety, epilepsy, etc., due to its impact on an individual’s educational, social, and employment. Since diagnosis is challenging and there is no cure, the goal...
This study examines the role of artificial intelligence (AI) in judicial decision-making, focusing on bail and sentencing contexts. We examined public perceptions of judges who use AI tools compared to those who rely solely on expertise. Using an exp...
This paper examines the uptake of AI-driven fraud detection systems among financial institutions in the UAE and Qatar, with a special focus on trust, transparency, and perceptions of fairness. Despite the promise of AI operations in identifying finan...
Artificial intelligence (AI) is rapidly shifting from experimental pilots to mainstream clinical infrastructure, redefining how evidence, accountability, and ethics intersect in healthcare. This narrative review integrates insights from peer-reviewed...
Fairness Artificial Intelligence (AI) aims to identify and mitigate bias throughout the AI development process, spanning data collection, modeling, assessment, and deployment—a critical facet of establishing trustworthy AI systems. Tackling dat...
The emerging capabilities of artificial intelligence (AI) and the systems that employ them have reached a point where they are integrated into critical decision-making processes, making it paramount to change and adjust how they are evaluated, monito...
Background/Objectives: The research addresses algorithmic bias in deep learning models for cardiovascular risk prediction, focusing on fairness across demographic and socioeconomic groups to mitigate health disparities. It integrates fairness-aware a...
Recently, AI software has been rapidly growing and is widely used in various industrial domains, such as finance, medicine, robotics, and autonomous driving. Unlike traditional software, in which developers need to define and implement specific funct...
Deepfake-style AI tutors are emerging in online education, offering personalized and multilingual instruction while introducing risks to integrity, privacy, and trust. This study aims to understand their pedagogical potential and governance needs for...
The integration of Artificial Intelligence (AI) into manufacturing is transforming the industry by advancing predictive maintenance, quality control, and supply chain optimisation, while also driving the shift from Industry 4.0 towards a more human-c...
As organizations increasingly turn to machine learning for customer segmentation and targeted marketing, concerns about fairness and algorithmic bias have become more urgent. This study presents a comprehensive fairness audit and mitigation framework...
Artificial intelligence (AI) powers breakthroughs in language processing, computer vision, and scientific discovery; yet, the increasing complexity of frontier models makes their reasoning opaque. This opacity undermines public trust, complicates dep...
This study proposes a systematic framework for establishing ethical guidelines for GeoAI (Geospatial Artificial Intelligence), which integrates AI with spatial data science, GIS, and remote sensing. While general AI ethics have advanced through the O...
The emergence of generative AI presents complex challenges to existing copyright regimes, particularly concerning the large-scale use of copyrighted materials in model training. Legal disputes across jurisdictions highlight the urgent need for a bala...
Artificial intelligence (AI) is widely used in healthcare applications to perform various tasks. Although these models have great potential to improve the healthcare system, they have also raised significant ethical concerns, including biases that in...
Background/Objectives: Federated learning (FL) offers a way for healthcare institutions to collaboratively train machine learning models without sharing sensitive patient data. This systematic review aims to comprehensively synthesize the ethical dim...
The integration of artificial intelligence into criminal sentencing decisions represents one of the most consequential applications of algorithmic systems in contemporary governance. While AI-assisted risk assessment tools promise enhanced consistenc...
With the development of decentralized artificial intelligence (AI) networks, federated learning (FL) has received extensive attention for its ability to enable collaborative modeling without sharing raw data. However, existing methods are prone to co...
This study introduces a novel Interleaved Fusion Learning (IFL) methodology leveraging transfer learning to generate a family of models optimized for specific datasets while maintaining superior generalization performance across others. The approach...
Bioarchaeology continues to generate growing volumes of data from finite and often destructively sampled resources, making data reusability critical according to FAIR principles (Findable, Accessible, Interoperable, Reusable) and CARE (Collective Ben...
Hospitals must allocate beds and staff effectively under volatile arrival patterns, where scheduling errors can cause preventable harm. This study introduces a fairness-aware, forecast-informed reinforcement learning framework for hospital scheduling...
The EU Artificial Intelligence Act (Regulation (EU) 2024/1689) designates AI systems used in life and health insurance underwriting as high-risk systems, imposing rigorous requirements for bias testing, technical documentation, and post-deployment mo...
As artificial intelligence (AI) is increasingly used in classrooms, tutoring systems, and learning platforms, it is essential that these tools are not only powerful, but also easy to understand, fair, and supportive of real learning. Many current AI...
As large language models (LLMs) increasingly enter high-stakes decision-making contexts, questions arise about their suitability in domains requiring normative judgment, such as judicial sentencing. This study investigates whether LLMs exhibit bias w...
The increasing adoption of artificial intelligence algorithms is redefining decision-making across various industries. In the financial sector, where automated credit granting has undergone profound changes, this transformation raises concerns about...
Objective: In this paper, we explore the correlation between performance reporting and the development of inclusive AI solutions for biomedical problems. Our study examines the critical aspects of bias and noise in the context of medical decision sup...
Data quality is fundamental to scientific integrity, reproducibility, and evidence-based decision-making. Nevertheless, many datasets lack transparency in their collection and curation, undermining trust and reusability across research domains. This...
Fairness-aware classification with respect to sensitive attributes, such as gender and race, is one of the most important topics in machine learning. Although numerous studies have made outstanding progress through various approaches, one key limitat...
Asymmetries in data distributions and performance across subgroups can induce systematic unfairness in real-world systems. A variety of previous studies have significantly ameliorated the fairness of deep learning models; however, most of them necess...
The use of algorithmic models or systems in criminal justice is increasing day by day, yet the bias in these algorithms can perpetuate historical inequities, especially in predictive tools like COMPAS. This literature survey examines 30 studies addre...
This study presents a fairness-aware framework for modeling the Probability of Default (PD) in individual credit scoring, explicitly addressing the trade-off between predictive accuracy and fairness. As machine learning (ML) models become increasingl...
This paper explores the rise in voice-based social media as a pivotal transformation in digital communication, situated within the broader era of chatbots and voice AI. Platforms such as Clubhouse, X Spaces, Discord and similar ones foreground vocal...
To achieve the energy transition, energy and energy efficiency are becoming more and more important in society. New methods, such as Artificial Intelligence (AI) and Machine Learning (ML) models, are needed to coordinate supply and demand and address...
Background: The emergence of artificial intelligence (AI) has triggered a global transformation, with the healthcare sector experiencing significant disruption and innovation. In current public health practice, AI is being deployed to power various a...
Machine learning-based decision support systems bring relief and support to the decision-maker in many domains such as loan application acceptance, dating, hiring, granting parole, insurance coverage, and medical diagnoses. These support systems faci...
Most research on fairness in Machine Learning assumes the relationship between fairness and accuracy to be a trade-off, with an increase in fairness leading to an unavoidable loss of accuracy. In this study, several approaches for fair Machine Learni...
This study addresses fairness in machine learning for student academic performance prediction using the UCI Student Performance dataset. We comparatively evaluate logistic regression, Random Forest, and XGBoost, integrating the Synthetic Minority Ove...
This study presents a next-generation clinical decision-support architecture for Clinical Decision Support Systems (CDSS) focused on emergency triage. By integrating Large Language Models (LLMs), Federated Learning (FL), and low-latency streaming ana...
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