AI-Driven Modeling of Individual Differences in Social Fairness Preferences: Integrating Genetic Variability, Neural Activity, and Reinforcement Learning

Abstract

Social fairness preferences are not uniform across individuals, yet many computational systems treat fairness as a stable, universal, or group-level construct. This mismatch creates risks for allocation, moderation, insurance, public service delivery, and human-machine collaboration, where models may misread moral motivation, adapt poorly to minority norms, or reinforce institutional bias. This paper develops a system-level research agenda for AI-driven modeling of individual differences in social fairness preferences by integrating genetic variability, neural activity, and reinforcement learning. It argues that fairness preferences should be represented as dynamic, context-sensitive policies shaped by biological predispositions, neural valuation and control systems, social learning, and institutional feedback. The paper examines architectural requirements for multi-level data integration, including secure ingestion of behavioral, neuroimaging, and genomic signals; hybrid inference that combines reinforcement learning with causal and interpretable modeling; and governance infrastructure for consent, provenance, auditability, and redress. It further analyzes structural trade-offs among personalization, privacy, robustness, sustainability, and policy legitimacy. The discussion emphasizes that genetic and neural evidence should inform system design without falling into determinism, and that reinforcement learning can serve as a computational bridge between individual difference and adaptive social behavior. The concluding argument is that fairness-aware AI must be governed as a socio-technical infrastructure, not merely optimized as a predictive model, because its legitimacy depends on contestability, accountability, and alignment with plural moral communities.