Responsible artificial intelligence in organizational decisions: ethical implications, transparency, and user trust

Abstract

Introduction: Artificial intelligence increasingly participates in organizational decisions involving selection, prioritization, risk assessment, and resource allocation. This expansion creates potential benefits but also risks related to opacity, bias, reduced autonomy, and unclear accountability, all of which can undermine user trust and organizational legitimacy. Method: A narrative review of Scopus-indexed literature published from 2021 to 2026 was conducted. Search concepts included responsible AI, AI ethics, explainable AI, transparency, accountability, trust, and organizational decision making. Articles with a DOI were selected when they contributed conceptual or empirical evidence on legitimacy, explanation, fairness, human oversight, and governance in AI-supported decisions. The review organized the evidence around ethical principles, transparency, explainability, user trust, human oversight, and accountability. Results: The literature indicated that trust is strengthened when users receive understandable explanations, information about performance and uncertainty, fairness safeguards, and accessible appeal mechanisms. Transparency alone did not guarantee comprehension or calibrated trust. Human oversight was meaningful only when people had genuine authority to challenge or reverse algorithmic recommendations. Clear assignment of responsibility for data, validation, monitoring, and incident response also supported legitimacy and accountability. Conclusions: Responsible organizational AI requires ethics, transparency, and trust to be integrated across the decision cycle. Organizations should use risk-proportionate controls, meaningful human oversight, and identifiable accountability structures to reduce both excessive reliance on automated outputs and indiscriminate rejection of useful algorithmic support.