ETHICAL AI IN HUMAN RESOURCE AND FINANCIAL DECISION-MAKING: A BENCHMARK-INFORMED FRAMEWORK FOR FAIRNESS, TRANSPARENCY AND ACCOUNTABILITY IN AUTOMATED SYSTEMS

Abstract

Automated decision systems now shape consequential human-resource and financial outcomes, including recruitment, candidate screening, employee analytics, credit underwriting, fraud detection and portfolio risk assessment. Yet adoption has advanced faster than the evidentiary routines needed to demonstrate fairness, transparency, privacy protection and accountable human control. This article develops and evaluates a benchmark-informed ethical AI framework for HR and financial decision-making using four complementary evidence streams: recent sector surveys and institutional reports, peer-reviewed and public case evidence on algorithmic disparity, reproducible experiments on public credit-risk benchmarks, and structured coding of major governance instruments. The empirical component analyzes the South German/German Credit benchmark and the FICO HELOC Explainable Machine Learning Challenge dataset using transparent and ensemble classifiers, cross-validation, calibration loss, subgroup fairness metrics and permutation importance. The German Credit analysis shows that similar discrimination performance can coexist with materially different fairness profiles: logistic regression achieved mean ROC-AUC of 0.783 but displayed an age-based equal-opportunity difference of -0.122, while random forests achieved ROC-AUC of 0.785 with substantially smaller equal-opportunity difference (-0.004) but lower balanced accuracy. On FICO HELOC, logistic regression achieved holdout ROC-AUC of 0.793 and cross-validated ROC-AUC of 0.796, indicating that interpretable models can be competitive for some credit-risk tasks; however, the absence of protected attributes prevents a complete fairness audit. Source-derived evidence further shows an adoption-governance gap: organizations report extensive AI use in HR and finance, while many lack policy clarity, measurement routines or full understanding of model behavior. The proposed FAIR-HITL framework integrates fairness-aware modelling, explainable AI, privacy-preserving data governance, human-in-the-loop review, regulatory compliance and post-deployment redress into a single auditable lifecycle. The study contributes a reproducible cross-sector methodology and an implementation-ready governance model for high-stakes automated decision systems.