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<p>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.</p>

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Algorithmic fairness and bias mitigation in financial artificial intelligence: scoping review

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月20日

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  • Marcelo Wecchi,
  • Lilian Berton

摘要

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.