Intelligent Management and Artificial Intelligence: Trends, Challenges, and Opportunities, Vol.1

Purpose: This study examines the ethical challenges of AI-driven decision-making in high-stakes domains such as autonomous vehicles and algorithmic tenant screening. It questions whether AI can act morally or simply optimize for efficiency, assessing its alignment with human ethical values. Need for the Study: As AI systems increasingly shape critical aspects of life—transportation, housing, employment, there is growing concern about their ethical impact. While promising fairness, AI often amplifies societal biases. Existing frameworks lack transparency and safeguards, making governance essential. Methodology: A mixed-methods approach is applied, including case study analysis, large-scale ethical simulations, and cross-cultural comparisons. Empirical data from the MIT Moral Machine experiment and AI-generated moral decisions are analyzed using exploratory factor analysis and hierarchical clustering. Findings: The study reveals sharp differences between human and AI ethics. AI models exhibit stronger biases, favoring younger, wealthier, and law-abiding individuals more than humans. Cultural differences are also evident: Western societies tend toward utilitarianism, while collectivist cultures factor in broader moral dimensions. The SafeRent case showed that AI-driven screening disproportionately disadvantaged minority applicants, reinforcing inequality. Practical Implications: The findings emphasize the need for human oversight in AI systems. Hybrid frameworks that combine algorithmic efficiency with ethical constraints are recommended. Policymakers and business leaders must enforce transparent, culturally aware AI standards that promote fairness. Ethical governance of AI must balance global norms with regional values to ensure equitable outcomes.