(untitled) (Journal of responsible AI & ethics, 2026)
Artificial intelligence (AI) has rapidly transformed various domains including healthcare, finance, governance, education, and transportation. While these technological advancements have enhanced efficiency and decision-making capabilities, they have also raised serious ethical concerns regarding accountability, bias, transparency, and societal impact. One of the most widely discussed solutions to these concerns is the implementation of Human-in-the-Loop (HITL) systems, where human oversight is integrated into AI decision-making processes. Human-in-the-loop frameworks ensure that humans remain responsible for supervising, validating, and correcting algorithmic outputs, thereby reducing the risks associated with autonomous decision-making. This research paper explores the ethical significance of human oversight in AI systems and analyzes how human involvement contributes to fairness, accountability, and transparency. The study examines real-world examples from healthcare diagnostics, financial risk assessment, and automated decision systems to illustrate the practical implications of human-in-the-loop models. Using both qualitative and quantitative research approaches, this paper evaluates how human supervision influences the reliability and ethical compliance of AI systems.