Value conflict refers to a situation in which two or more moral principles, beliefs, or priorities come into opposition, making it difficult to choose the most appropriate course of action. In the context of Artificial Intelligence (AI), value conflict emerges because AI systems increasingly participate in decision-making processes that affect individuals, organizations, and society. AI is designed to perform tasks efficiently based on data and algorithms, whereas human beings make decisions influenced by ethical principles, emotions, culture, and social values. As AI becomes integrated into healthcare, education, finance, law enforcement, transportation, and governance, conflicts between machine-driven efficiency and human values become more prominent. One of the major value conflicts in AI concerns fairness versus efficiency. AI systems often optimize outcomes based on statistical accuracy or productivity. However, such optimization may unintentionally discriminate against certain individuals or groups due to biased training data. This creates ethical concerns regarding equality, justice, and non-discrimination. Decisions related to hiring, loan approvals, or criminal sentencing may produce unfair outcomes if bias is embedded in algorithms. Another important ethical challenge is privacy versus technological advancement. AI relies heavily on large volumes of personal data to improve its performance. Continuous data collection and surveillance can violate individuals' right to privacy and personal autonomy. The misuse of personal information for commercial purposes or unauthorized surveillance raises serious ethical and legal concerns. Transparency versus complexity is another value conflict. Many advanced AI models operate as "black boxes," where even developers cannot fully explain how decisions are reached. Lack of explainability reduces public trust and makes it difficult to hold anyone accountable for incorrect or harmful decisions.