FROM ALGORITHMIC OPACITY TO GENDER-RESPONSIVE ACCOUNTABILITY: AN EXPERT-VALIDATED G-XAI AUDIT FRAMEWORK FOR HIRING AND CREDIT DECISIONS

Artificial intelligence increasingly mediates access to employment and credit, while gendered patterns may remain embedded in historical labels, proxy variables, optimization objectives, and organizational routines. This study develops an expert-validated, stakeholder-oriented Gender-Responsive Explainable Artificial Intelligence (G-XAI) audit framework for hiring and credit decisions. A pilot PRISMA 2020 evidence synthesis identified 86 records, removed 12 duplicates, screened 74 records, assessed 36 full-text reports, and included 20 DOI-verified peer-reviewed studies. The resulting framework was assessed through structured expert validation informed by modified Delphi principles, using quantitative ratings, qualitative comments, and an independent methodological review. The evidence synthesis showed that fairness metrics alone are insufficient, while expert validation supported the framework’s relevance and emphasized gender and intersectional analysis, explainability linked to accountability, human oversight, documentation, contestability, and remediation. Experts also identified implementation challenges and instrument-design issues requiring further refinement. Because validation records differed in analytical level, findings were synthesized descriptively rather than treated as formal Delphi consensus. Grounded in stakeholder theory and SDG 5, the framework provides an eight-stage lifecycle architecture for integrating fairness, explainability, governance, and gender-responsive accountability.