Jul 2026· AI and Ethics· Vol 6· 0 citations· 103 references
Computer Science
TL;DR
It is mapped how the literature addresses fairness in finance, the metrics employed, the financial contexts considered, and the effectiveness of mitigation techniques, to synthesize existing knowledge, identify methodological gaps, and provide guidance for future research and policy development.
Abstract
The growing adoption of artificial intelligence in the financial sector has intensified concerns regarding unfair discrimination across diverse systems. Under increasing regulatory and accountability pressures, ensuring fairness and transparency in AI-driven decision-making has become a critical challenge. Our aim is to map how the literature addresses fairness in finance, the metrics employed, the financial contexts considered, and the effectiveness of mitigation techniques. This review also seeks to synthesize existing knowledge, identify methodological gaps, and provide guidance for future research and policy development. We considered peer-reviewed articles focused on AI in finance and fairness, prioritizing studies from 2023–2026 or from 2016–2022 with at least 100 citations. The papers were collected from CAPES, Elsevier, Google Scholar, Scopus, Web of Science, and gray literature such as ArXiv. We performed automated screening, AI-based refinement (Gemini, Perplexity, Copilot), and systematic extraction of technical dimensions using a results spreadsheet for thematic categorization of articles considered relevant. We identified 17,019 records, resulting in a final corpus of 99 articles for the synthesis of the work and 45 supporting documents for contextualizing finance and equity. We answered seven research questions related to datasets, machine learning, and equity approaches, as well as evaluation metrics. We found a predominance of studies on credit risk and credit scoring. There was a consolidation of technical approaches and identification of the need for standardized metrics, with greater emphasis on intersectionality and causality for sensitive data.
This study synthesises technical and stakeholder dimensions of AI in property valuation using a structured qualitative approach via SLR and proposes a novel hybrid framework that integrates stakeholder trust factors with model precision to enhance both reliability and acceptance of AI tools.
Wajhat Ali, D. Samarasinghe, Zhenan Feng et al.· Urbanization, Sustainability...· 0 citations
This study presents a systematic literature review of 68 peer-reviewed articles (2015–2025) on artificial intelligence in tax compliance and evasion mitigation. Using the PRISMA 2020 protocol and textometric analysis via IRAMUTEQ software, we map publication trends, geographic distribution, and three research paradigms: machine learning and predictive modeling; artificial intelligence, technology and tax compliance; and government, financial development and revenue administration. The CIMO (Context–Intervention–Mechanism–Outcome) framework structures our synthesis of how institutional conditions shape intervention design and why identical technologies produce divergent outcomes across settings. While existing reviews have focused primarily on detection metrics without theorizing institutional boundary conditions, behavioral dynamics without addressing governance capacity, or ethical deficits without a theoretical framework, this study constructs the Adaptive AI Tax Compliance Framework (AAITCF), a context-sensitive implementation roadmap differentiated across three institutional maturity tiers. The results indicate that AI achieves high detection accuracies in digitally mature economies, yet effectiveness is contingent on data quality, governance capacity, and organizational readiness. Developing countries face structural asymmetries, infrastructural deficits, and human capital gaps that constrain algorithmic performance even where technical sophistication is high. The AAITCF treats context as constitutive of intervention effectiveness and identifies underexplored areas regarding causal pathways from AI deployment to long-term institutional change, taxpayer trust, and equitable fiscal governance.
Houda Zaim, Siham Sahbani· Journal of Risk and Financia...· 0 citations
Artificial intelligence has diffused rapidly through the recruitment function, from automated résumé screening and chatbot-led candidate engagement to algorithmic assessment and predictive analytics, and vendors and adopters advance strong claims about efficiency and quality-of-hire gains. Simultaneously, high-profile failures have made algorithmic hiring a focal case in debates about automated discrimination and its regulation. This paper reviews the multidisciplinary literature on AI in recruitment — spanning human resource management, information systems, computer science research on algorithmic fairness, and the emerging regulatory scholarship — to assess what is credibly known about its benefits, its risks, and the conditions that separate the two. The review finds robust evidence for process-efficiency gains but thin and mixed evidence for quality-of-hire improvement; a well-established taxonomy of bias mechanisms (training data bias, proxy discrimination, and feedback loops) with documented instances in deployed systems; and an emerging governance literature converging on auditability, human oversight, and outcome monitoring as the practices that condition whether adoption helps or harms diversity outcomes. The paper develops a governance-centred framework for HR practice, maps it against incoming regulation including the EU AI Act's classification of employment AI as high-risk, and sets out a research agenda focused on the gap between vendor claims and independently verifiable outcomes.
Mehmoona Akram, Syeda Fatima Hussain, R. Anwar· Journal of Management Resear...· 0 citations
This study examines the transformative potential of Artificial Intelligence (AI) and Decentralized Finance (DeFi) in reshaping global financial systems and reducing economic inequality. The purpose of the study is to explore how AI-driven DeFi can improve financial inclusion by expanding access to secure, transparent, and decentralized financial services for underserved populations while addressing challenges such as algorithmic bias, technological inequality, and regulatory uncertainty. Using a qualitative methodology, the study reviews existing literature, analyzes emerging trends, and examines selected applications of AI and DeFi technologies. The analysis focuses on how AI enhances DeFi through automation, predictive analytics, personalized services, fraud detection, and transaction cost optimization. The findings show that AI-powered DeFi systems can promote economic empowerment, particularly in emerging economies, by lowering financial barriers and increasing access to financial services. However, inadequate infrastructure, limited digital literacy, and biased algorithms may deepen existing inequalities if left unaddressed. The study concludes that inclusive policy frameworks, investment in digital literacy, stronger blockchain infrastructure, and measures to reduce algorithmic bias are necessary to ensure equitable access to AI-DeFi innovations. Future research should empirically evaluate AI-DeFi applications across different socioeconomic contexts to determine their effectiveness in building inclusive and resilient financial systems.
Sani Abdullahi Sule· International Journal of Sus...· 0 citations
Abstract The integration of Artificial Intelligence (AI) into competition authorities to detect anti-competitive practices entails inherent ethical risks, such as algorithmic bias and excessive dependence on technology providers. To mitigate these risks, this article investigates how thirty-five regulatory authorities, ranked in the 2023 GCR Enforcement Rating, address these challenges. A document analysis, carried out using ATLAS.ti25, compares these organizations based on five ethical principles (transparency, accountability, fairness and equity, robustness and security, and privacy), grounded in consolidated frameworks in AI ethics and protection bioethics. The results reveal a critical disparity in regulatory maturity: greater technical rigor is observed in operational ethics principles (robustness, privacy, and security) than in social ethics principles. Substantial deficiencies persist in transparency, accountability, and fairness/equity. This gap points to a deficit in the core principle of explainability (encompassing both intelligibility and accountability). The main cause lies in the absence of clear procedures and limited disclosure regarding the use of AI in the core activities of these organizations. The study concludes that strengthening ethical leadership and establishing organizational accountability mechanisms are essential to ensure the fair and transparent application of AI in economic regulation.
Mayla Cristina Costa Maroni Saraiva, Fátima de Souza Freire· Revista de Administración Pú...· 0 citations
A PRISMA 2020-guided systematic literature review draws on 82 studies selected from 493 records retrieved from Scopus and Web of Science and reveals a structural disconnect in the fairness-in-NLP and HCAI governance literature.
Asmae El Moutafail, Khalid Belkhoutout· EPJ Web of Conferences· 0 citations
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