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Synergizing Fairness, Cooperation, and Transparency: A Unified Paradigm for Trustworthy Distributed Multi-Agent AI
Abstract
As artificial intelligence (AI) transitions from centralized single-agent architectures to highly distributed, collaborative networks, three fundamental challenges emerge: ensuring fair collective decision-making, enabling adaptive multi-agent coordination, and providing real-time, human-understandable transparency. This paper presents a unified, humanized framework that synergizes these domains into a trustworthy ecosystem. We synthesize: (1) distributed facility location mechanisms to mathematically aggregate decentralized preferences under strict distortion bounds, (2) Diametric Coordination Graphs (DiaCoG) to govern cooperative multi-agent reinforcement learning (MARL) by dynamically leveraging both observation consistency and discrepancy, and (3) Fast Concept-based Counterfactual Explanations (FCCE) to deliver near- instantaneous, concept-level explanations (< 10^−5 seconds) for deep vision models. By bridging axiomatic social choice theory, adaptive multi-agent networks, and real-time interpretability, we provide a mathematically rigorous blueprint for scalable, human-aligned, and secure distributed AI systems.
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