FAIRNESS IS NOT INNATE A Three-Variable Framework for Human and Artificial Moral Behavior

This paper proposes a unified, empirically-grounded framework for understanding fairness in humans, primates, and artificial intelligence systems. Drawing on evolutionary biology, cognitive neuroscience, behavioral economics, and moral philosophy, it argues that fairness is not an innate human trait but an emergent property of three measurable variables: Intelligence (I), Accurate Data (D), and Perceived Survival Safety (S). The framework is formalized as F(t) = I(t) × D(t) × S(t), where each variable has a precise mathematical definition. A time-evolution model captures how variables dynamically influence one another, revealing poverty as a self-reinforcing cascade rather than a moral failure. The framework is supported by seven independent studies across neuroscience, behavioral economics, and conflict research. Sensitivity analysis confirms model robustness. Edge cases and boundary conditions are examined. A Fairness Assessment Questionnaire operationalizes the model as a practical measurement tool. Finally, the framework is extended to AI alignment, where the simplification S = 1 yields F_AI = I × D, with direct implications for data-centric AI development and reward function design.