Cross-domain Ethical AI: A Systematic Review of Sector-specific Challenges in Healthcare, Finance, and Criminal Justice Applications
Abstract
Artificial intelligence (AI) is increasingly embedded in high-stakes domains such as healthcare, criminal justice, and finance, delivering significant operational and societal benefits while simultaneously introducing complex ethical challenges. This study critically examines the ethical implications of AI deployment across these sectors using four foundational ethical principles: beneficence, non-maleficence, autonomy, and justice. In healthcare, AI-driven systems support early diagnosis, personalized treatment, and clinical decision-making; however, they raise serious concerns related to patient autonomy, informed consent, data privacy, and cybersecurity. Within the criminal justice system, predictive analytics and risk assessment tools influence policing strategies and sentencing decisions, offering efficiency gains but also amplifying risks of algorithmic bias, opacity, and the reinforcement of historical inequalities. In the financial sector, applications such as algorithmic trading, fraud detection, and automated credit scoring enhance speed and accuracy, yet may compromise transparency, accountability, and equitable access to financial services. Through a comparative, cross-domain analysis, this study identifies common ethical challenges including bias mitigation, explainability, and accountability while also underscoring sector-specific risks and governance requirements. The findings emphasize that responsible AI adoption necessitates comprehensive regulatory frameworks, interdisciplinary collaboration, stakeholder oversight, and continuous ethical auditing to ensure that technological innovation remains aligned with human values, social justice, and long-term societal well-being.
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