arXiv:2605.22827v1 Announce Type: cross Abstract: In large-scale AI systems, allocating scarce resources such as GPU compute time and bandwidth among multiple agents is a critical challenge. Conventional policies focus on efficiency metrics, potentially leading to dominance concentration that undermines system diversity and stability. We propose Computable Fair Division (CFD), a framework that reinterprets the Boltzmann-Softmax function not as a selection tool but as a probabilistic resource allocation mechanism, redefining the inverse temperature parameter $\beta$ as a computable control vari
As AI systems scale and become critical infrastructure, the challenge of fair resource allocation intensifies, making this a timely innovation.
This framework addresses a fundamental constraint in the development and governance of large-scale AI, moving beyond efficiency to incorporate fairness, which can impact system stability and societal acceptance.
The conventional approach to AI resource allocation shifts from purely efficiency-driven to one that systematically integrates fairness, potentially altering the competitive landscape and access to compute.
- · Smaller AI research groups
- · Underrepresented AI applications
- · Cloud providers with refined allocation algorithms
- · AI ethics and governance frameworks
- · Dominant AI labs relying solely on compute hoarding
- · Systems focused purely on short-term efficiency gains
- · Legacy AI resource management policies
AI resource allocation becomes more equitable, fostering diversity in research and development.
Increased diversity in AI leads to a broader range of applications and potentially more robust, less biased models.
Fairer access to compute could decentralize AI development, reducing the power of current compute-rich entities and potentially impacting national AI strategies.
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Read at arXiv cs.AI