Decider Model Aggregation for Trusted AI Consensus
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Solution Overview
Problem
Cloud-based decentralized networks face challenges in trust due to lack of transparency in decision-making processes, making it difficult to verify the correctness of outcomes and eroding confidence among participants.
Innovation Solution
A consensus-based network of decision-focused computational models, including AI models, where a decider model receives requests, forwards input information to multiple computational models, aggregates their singular outcomes, and communicates a trusted outcome response to the requestor.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple nodes participate in computations in a cloud-based decentralized network, then computational capability and scalability are improved, but transparency in decision-making processes deteriorates
Solution Approach 1:
The system segments the decision-making process into distinct components: individual node computations, aggregation operations, and verification mechanisms. Each node performs independent computations while the aggregation layer maintains transparency by recording and exposing the decision-making trajectory, thus resolving the contradiction between distributed computational capability and decision transparency.
Solution Approach 2:
The system implements feedback mechanisms where nodes receive verification signals about their computations and decisions. The aggregation layer provides feedback on the overall decision-making process transparency, allowing nodes to adjust their computations while maintaining an auditable trail of decisions, thereby preserving both computational scalability and transparency.
2Reliability
If multiple nodes participate in computations, then system resilience is improved, but verification of outcome correctness becomes more difficult
Solution Approach 1:
The aggregation layer acts as an intermediary between individual nodes and the final outcomes. It collects computations from multiple resilient nodes, verifies their correctness through structured aggregation operations, and produces verified outcomes. This intermediary mechanism maintains system resilience while simplifying verification by centralizing the validation process.
Solution Approach 2:
The system replaces complex manual verification mechanisms with automated computational verification processes. The aggregation layer uses algorithmic methods to verify outcome correctness across multiple nodes, substituting mechanical verification with computational validation that scales with system resilience.
3Adaptability or versatility
If decentralized computation is implemented, then system flexibility is improved, but trust in network outcomes deteriorates
Solution Approach 1:
The aggregation layer provides universal functionality that works across different node configurations and computation types. It maintains system flexibility by accommodating diverse node operations while building trust through consistent, verifiable aggregation processes that produce reliable outcomes regardless of the specific decentralized computation details.
Solution Approach 2:
The system uses feedback mechanisms at the aggregation layer to build trust in decentralized outcomes. Verification signals and transparency reports are provided to participants, allowing them to confirm the reliability of network outcomes while maintaining the flexibility of decentralized computation architectures.
Data Source
AI summary
There is disclosed a method and apparatus for generating desired trusted outcomes in a consensus-based network having a plurality of decision-focused computational models and an orchestration engine configured to organize the operation of these models. The orchestration engine is configured to receive requests from requestors and to generate input information related to these requests. The orchestration engine includes at least one decider model, which comprises an invoke module and an aggregation module. The invoke module forwards the input information to the decision-focused computational models, and the aggregation module receives and aggregates the singular outcomes to produce the desired trusted outcome. The decider model then communicates this outcome to the requestor.