<![CDATA[The integration of artificial intelligence into executive financial decision-making has introduced new ethical challenges, creating complex trade-offs between financial optimality and ethical considerations. Current methodologies remain inadequate for systematically quantifying these dilemmas in strategic financial contexts. This study introduces a novel scenario-based simulation framework to measure and analyze trade-offs between financial performance and ethical consequences in AI-augmented decision environments. The research employs computational simulation modeling across ten strategic financial scenarios, involving configured AI agents and behaviorally profiled human agents executing 1,000 iterations per scenario under controlled conditions. The results indicate that ethical trade-offs are highly context-dependent, with 40% of scenarios showing a statistically significant negative correlation between financial and ethical outcomes. In high-conflict scenarios, AI-driven ethical considerations improve ethical scores by 34% while incurring an 18% opportunity cost in financial performance. Executive behavioral biases, particularly overconfidence and loss aversion, significantly degrade ethical outcomes beyond the improvements achieved through AI ethical calibration. Methodologically, this study contributes a replicable and extensible simulation-based approach for systematically quantifying ethical–financial trade-offs under controlled yet behaviorally grounded conditions. Practically, the findings provide actionable managerial and governance implications by informing the design of ethical audit mechanisms, bias mitigation strategies, and AI calibration policies in financial decision-making. The framework enables executives and regulators to conduct proactive ethical audits of AI systems, bridging the gap between theoretical AI ethics and practical financial governance.]]>
