algorithmic ethics

This study explores how AI is perceived and operationalized in credit risk assessment within Iranian banking institutions, with a particular focus on the experiences of electronic banking professionals in Tehran. Drawing on grounded theory methodology and semi-structured interviews with 38 practitioners from both public and private banks, the research reveals a complex landscape of technological promise and institutional constraint. Participants emphasized the efficiency, consistency, and expanded analytical reach afforded by AI models, particularly in leveraging alternative data and enhancing fraud detection. However, these benefits are tempered by operational challenges, including fragmented data systems, outdated IT infrastructure, and opaque algorithmic outputs. Ethical and regulatory concerns—especially surrounding algorithmic bias, accountability, and the absence of formal oversight—emerged as significant barriers to responsible deployment. Moreover, organizational resistance, hierarchical decision-making structures, and cultural skepticism toward automation further complicate adoption. The findings suggest strong practitioner support for hybrid decision-making models that integrate AI capabilities with human expertise. This model offers a viable pathway toward responsible innovation, balancing the computational advantages of AI with the contextual judgment and ethical sensitivity of human agents.

This study examines the intersection of artificial intelligence, global governance, and disaster risk management through a qualitative investigation of 92 Iranian experts across disciplines such as geoinformatics, emergency planning, and environmental engineering. While AI offers a significant promise for enhancing early warning systems, damage assessments, and real-time decision-making, its integration into DRM systems remains constrained by fragmented data infrastructures, institutional silos, and geopolitical exclusions. Participants underscored AI’s potential to improve response coordination and risk forecasting, but emphasized the need for robust data governance, algorithmic transparency, and capacity building. The study highlights critical ethical and political concerns—particularly in countries like Iran facing technological marginalization due to sanctions and limited access to global data ecosystems. Drawing on grounded theory and thematic analysis, the research identifies institutional fragmentation, interoperability barriers, and normative governance deficits as primary obstacles to AI-enabled DRM. It argues for a globally coordinated approach grounded in justice, inclusivity, and human-centered design.