A “black box” or a transparent algorithm: an analytical review of sources on the ethics of artificial intelligence

The rapid advancement of artificial intelligence (AI) technologies presents significant ethical challenges that demand attention from both the scientific community and society. This article reviews AI ethics research published over the past five years, identifying key issues and barriers to implementing ethical principles across various domains. The aim of the study is to identify key problem areas and limiting factors that hinder the implementation of ethical principles in various areas of application of computational intelligent systems. The sampling methodology includes a systematic content analysis of source metadata with subsequent data clustering and thematic modeling implemented using NLP tools. The work pays special attention to the fundamental contradictions between theoretical provisions and their practical implementation, the dominance of the technocratic utilitarian approach over humanitarian expertise, as well as insufficient elaboration of mechanisms for distributing moral responsibility. In medicine, the opaque nature of machine learning models complicates clinical decision-making and accountability. In the legal field, AI used for risk assessment and judicial decisions raises concerns about algorithmic bias and fairness. A broader issue is the unclear distribution of moral responsibility in multi-stakeholder environments involving developers, users, and affected parties. Fairness and discrimination remain central concerns, as mathematical definitions of fairness often conflict and fail to reflect diverse cultural contexts. Data privacy is increasingly strained by AI’s demand for personal information, and global disparities in regulation hinder consistent protection. Military AI poses distinct ethical dilemmas, particularly regarding lethal autonomous systems lacking meaningful human control. The study highlights a technocratic trend in AI ethics discourse, prioritizing quantifiable performance metrics over nuanced ethical reflection. Emerging challenges at the intersection of AI applications create complex feedback loops beyond the reach of existing frameworks. Despite ongoing efforts, unresolved issues include achieving transparency, defining collaboration guidelines, and developing adaptive ethical models. The article calls for interdisciplinary research, greater stakeholder inclusion, and international cooperation to build AI systems aligned with human values and social well-being. The author declares no conflicts of interests.

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