Relational Autonomy and Non-Domination in the Age of AI: Load Minimization Theory as a Framework for Political Leadership

Contemporary political philosophy must address how leadership can preserve autonomy amid AI-induced relational power and vulnerability. Load Minimization Theory (LMT) provides a systematic way to quantify and compare relational burdens across agents, including AI systems. It minimizes epistemic burdens (uncertainty), relational tensions (friction), and burdens on capabilities (energy costs) to foster non-domination (Pettit) and relational autonomy (Mackenzie and Stoljar). LMT reframes transformational leadership as reducing epistemic uncertainty through shared visions, servant leadership as mitigating relational tensions via care, and shared leadership as ensuring fair distribution of capability burdens (Nussbaum). In AI-human coexistence, it identifies non-intentional domination arising from unchecked AI influence and proposes relational rest (autonomy-preserving harmony) as a political virtue. By treating LMT as an auxiliary tool, the paper contributes a unifying principle for relational ethics and democratic sustainability beyond human-centered paradigms. This paper argues that political leadership in the AI age should be understood as the practice of minimizing relational burdens to secure non-domination and relational autonomy.