From bias to balance: Integrating DEI in AI-driven financial systems to promote credit equity
Year: 2024
Type: article
Abstract: The integration of Artificial Intelligence (AI) in credit scoring systems marks a pivotal moment in financial inclusion, offering opportunities to address systemic inequities while presenting challenges in ensuring fairness. Traditional credit evaluation methods have historically marginalized underserved communities, perpetuating cycles of exclusion and economic disparity. AI-driven systems, when designed with Diversity, Equity, and Inclusion [DEI] principles, provide a pathway to transform these dynamics by promoting credit equity and expanding financial access. This paper explores the dual role of AI in modern credit scoring systems: as both a tool for perpetuating bias and a solution for mitigating it. It begins with an overview of how AI impacts underserved communities, analysing how algorithmic decision-making can inadvertently amplify discrimination if not carefully monitored. The discussion then focuses on strategies for embedding DEI principles into algorithm design, emphasizing the need for transparency, accountability, and the inclusion of diverse perspectives in AI development. These strategies are critical for identifying and correcting bias, ensuring that AI serves as a force for equity rather than exclusion. Additionally, the paper examines the use of non-traditional credit data, such as rental histories, utility payments, and employment records, as a means of bridging gaps in financial access for minorities. By expanding the criteria for creditworthiness, these alternative data sources challenge conventional models that often disadvantage marginalized populations. The paper concludes by highlighting the broader societal and ethical implications of integrating DEI into AI-driven financial systems, urging stakeholders to adopt inclusive practices that balance innovation with fairness.
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
SDG: Reduced inequalities
Open Access status: hybrid
OpenAlex ID: W4404642304