Abstract— This research explores the integration of human judgment, choices, and AI within decision-making frameworks with computer vision, focusing on a human-AI algorithm that balances both human intuition and computational power. This study focuses on factors such as human judgment, human choice techniques, and the opacity of human choices as observations to understand the cognitive and emotional factors influencing decision-making by robots in critical situations as key factors. Human observation is based on the situation judged by the frequency of environments (sensing), dimension of objects, and color perception.
This approach examines how human psychology, including biases and heuristics, interacts with AI systems to achieve optimal outcomes for real-time operations that foster trust and usability. This paper provides insights into designing algorithms that respect human autonomy while leveraging AI’s scalability, with implications for fields such as healthcare, robotics, and autonomous systems.