Abstract

This article develops a scientifically constrained philosophical proposal concerning the role of temporality in modeling human-like bias and affect in artificial intelligence. The central claim is not that temporality by itself constitutes emotion, suffering, or human subjectivity, but that a significant class of human biases and affective responses presupposes a temporally organized architecture in which duration, expectation, delay, internal-state deviation, reward trend, and temporal mismatch jointly constrain cognition and action. The argument integrates four research traditions. First, contemporary neuroscience and computational modeling suggest that subjective duration can emerge from salient perceptual change and from evolving neural states rather than from a dedicated internal clock. Second, work on temporal expectation shows that time actively structures cognition by modulating attention and the timing of working-memory utilization. Third, research on embodied and interoceptive time indicates that subjective temporality is deeply coupled to bodily and affective states. Fourth, current AI research shows both the continuing limitations of large language models in temporal reasoning and the emergence of temporal codes, homeostatic control, and temporally structured affect-like variables in reinforcement-learning systems. On this basis, the paper proposes a minimal temporal-affective architecture for human-aware AI, organized around perceptual duration, temporal expectation, temporal mismatch, internal-state control, and value-over-time. The paper concludes by distinguishing functional internal temporality from phenomenal subjectivity and by outlining a research program for building AI systems that better understand, anticipate, and respond to temporally structured human bias without reproducing human unfairness as a normative standard.