arXiv:2606.28217v1 Announce Type: new Abstract: 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
The proliferation of AI systems necessitates sophisticated mechanisms for managing contributions and rewards in multi-agent environments, especially when involving human values.
This framework offers a foundational approach to ethically and effectively managing AI cooperatives by embedding value constraints directly into reward allocation, which is crucial for broad AI adoption and trust.
The ability to screen and filter AI model updates based on heterogeneous value profiles allows for more aligned and trustworthy AI systems, moving beyond purely performance-based metrics.
- · AI cooperative platforms
- · AI ethics researchers
- · Organizations deploying value-sensitive AI
- · Decentralized AI initiatives
- · AI developers ignoring ethical frameworks
- · Centralized, opaque AI development models
Improved fairness and alignment in federated learning and decentralized AI systems.
Accelerated development of AI agents capable of operating within complex ethical and regulatory frameworks.
New legal and governance models emerging from the necessity of 'value-constrained' AI systems operating within diverse jurisdictions.
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Read at arXiv cs.LG