Towards Value-Constrained Credit Assignment in Fully Delegated AI Cooperatives
This work addresses the problem of fair and value-constrained reward distribution in decentralized AI systems involving multiple human stakeholders, but the proposal is conceptual with no empirical validation.
The paper proposes a credit assignment framework for AI cooperatives where human principals impose value constraints on model updates. It introduces value-conditioned gradient filtering and online marginal contribution signals within a traversal learning substrate to enable fine-grained, value-aligned reward allocation.
We propose a framework for reward allocation in fully delegated AI cooperatives where humans are represented by agents that contribute data and participate in model updates under heterogeneous value constraints. The key idea is to credit only those updates that remain admissible after screening them against each principal's value profile. We formulate value-conditioned gradient filtering, online marginal contribution signals, and cumulative revenue settlement within a traversal learning (TL) substrate. TL is especially attractive here because it performs decentralized backpropagation without the quality loss associated with aggregation-centric distributed learning and, we argue, offers a finer attribution substrate than FedAvg-style federated learning by preserving explicit traversal and gradient paths. The framework is positioned against data valuation, federated contribution estimation, personalized federated learning, and pluralistic alignment.