New AI fairness framework prevents GPU resource monopolies at scale

In large-scale AI systems, competition among multiple agents for scarce resources like GPU compute or bandwidth often leads to dominance concentration—a few agents hog resources while others starve, undermining system diversity and stability. A new paper on arXiv proposes Computable Fair Division (CFD), a framework that reimagines the Boltzmann-Softmax function—typically used for selection—as a probabilistic resource allocation mechanism. The key innovation is treating the inverse temperature parameter β as a computable control variable that governs the balance between efficiency and fairness. The authors demonstrate a Pareto frontier with a near-optimal 'Stability Corridor' where total system loss remains approximately constant across policy weights.

For dynamic environments, the paper introduces AHC++ (Adaptive Hard-Cap Controller++), which updates β in real time by feeding back the error between observed agent dominance and a policy-specified fairness target. Simulations show AHC++ effectively suppresses extreme dominance concentration under exogenous shocks while tracking fairness targets without substantial throughput degradation. Scalability is strong: a 100x increase in agents results in only about a 5.5x increase in execution time. The framework is open-source and could be directly applied to cloud GPU schedulers, blockchain consensus, or federated learning orchestrators.