Provenance Graph Nodes Compute Explainability Vectors
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Solution Overview
Problem
Existing systems for tracking provenance information fail to effectively assess the risk, confidence, and foundations of insights generated by diverse teams of humans and machines, lacking the ability to propagate confidence attributions and attribute propagation through complex networks of information sources and activities.
Innovation Solution
A method and system that utilize a directed graph data structure to compute explainability and provenance values by propagating values from upstream nodes, enabling confidence propagation and attribute propagation, and allowing dynamic updates and refutations of activities and sources within the provenance chain, thereby providing a graphical output for assessing the trustworthiness of information insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional provenance frameworks are used to track data lineage, then data origin tracking is achieved, but the ability to assess risk, confidence, and trustworthiness of generated insights is insufficient
Solution Approach 1:
The patent transforms provenance tracking from simple lineage recording to a multi-parameter assessment system by introducing confidence values, risk metrics, and explainability dimensions. Each node in the provenance graph is annotated with these parameters, allowing comprehensive evaluation of information trustworthiness while maintaining the underlying graph structure for scalability.
Solution Approach 2:
The patent introduces software agents as intermediaries that perform provenance analysis and compute trustworthiness metrics. These agents traverse the provenance graph, aggregate confidence values from multiple sources, and generate assessments without requiring direct complex interactions between all system components, thereby managing system complexity.
2Productivity
If diverse software agents are used to process data and generate insights, then productivity is improved, but accuracy, risk, and bias increase
Solution Approach 1:
The patent implements feedback mechanisms where software agents contribute their confidence values and provenance information to a centralized graph. This feedback loop allows the system to aggregate results from multiple agents, identify inconsistencies, and adjust confidence scores based on the reliability and agreement of different agents, thereby improving overall accuracy while maintaining high productivity.
3Loss of information
If provenance information is tracked through complex networks of activities and sources, then information lineage is captured, but the ability to propagate confidence attributions is lost
Solution Approach 1:
The patent adds a temporal and confidence dimension to the traditional provenance graph structure. By annotating nodes and edges with confidence values, timestamps, and risk metrics, the system preserves complete provenance information while enabling propagation of trustworthiness assessments through the network. This dimensional enrichment allows complex information networks to maintain both detail and computational tractability.
Data Source
AI summary
A computing machine accesses a directed graph representing one or more sequences of actions. The directed graph comprises nodes and edges between the nodes. Each node is either a beginning node, an intermediate node, or an end node. Each intermediate is downstream from at least one beginning node and upstream from at least one end node. Each beginning node in at least a subset of the beginning nodes has an explainability value vector. The computing machine computes, for each first node from among a plurality of first nodes that are intermediate nodes or end nodes, a provenance value representing dependency of an explainability value vector of the first node on the one or more nodes upstream from the first node. The computing machine computes, for each first node, the explainability value vector. The computing machine provides a graphical output representing at least an explainability value vector of an end node.