AI Value Reconciliation System for Discrepancy Resolution
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Solution Overview
Problem
The increasing use of multiple AI systems poses a risk of harm due to potential conflicts, discrepancies, and errors in data exchanged between these systems, which can lead to negative impacts in various contexts.
Innovation Solution
A computer-implemented method and system that reconciles values of a feature provided by different AI systems by collecting logs, identifying discrepancies, creating global information, and sending this information to AI systems with discrepancies to prevent negative impacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple AI systems are deployed to perform autonomous decisions, then the capability and coverage of AI applications is improved, but the risk of conflicts, discrepancies, and errors in data exchanged between systems increases
Solution Approach 1:
The patent introduces a reconciliation system as an intermediary component that sits between multiple AI systems. This mediator collects data from various AI systems, identifies discrepancies in their outputs, and generates reconciled values that can be shared back with the systems. The intermediary handles the complexity of coordinating multiple autonomous decision-making systems without requiring direct communication between each system.
Solution Approach 2:
The system implements feedback mechanisms where reconciled values generated by the reconciliation system are fed back to the individual AI systems. This allows the AI systems to receive corrected or adjusted data from their peers through the mediation of the reconciliation system, enabling continuous improvement and consistency across the distributed AI ecosystem.
2Measurement precision
If data from multiple AI systems is collected and reconciled, then the accuracy and consistency of information is improved, but the system complexity and processing requirements increase
Solution Approach 1:
The reconciliation system is segmented into distinct functional modules: a data collection component that gathers information from AI systems, a discrepancy identification module that detects conflicts in data, and a value generation module that creates reconciled values. This segmentation allows each component to handle specific tasks independently, making the overall complex system more manageable and easier to implement.
Solution Approach 2:
The system employs self-service mechanisms where AI systems automatically send their data to the reconciliation system, and the reconciled values are automatically distributed back to the systems. This automated process reduces the need for manual intervention and complex coordination protocols, simplifying the overall system architecture while maintaining high accuracy.
3Reliability
If discrepancies between AI system values are identified and corrected, then the reliability of AI decisions is improved, but the time and resources required for reconciliation increase
Solution Approach 1:
The system performs preliminary actions by continuously monitoring and collecting data from AI systems before discrepancies lead to erroneous decisions. The reconciliation system proactively identifies potential conflicts and corrects them before they propagate through the system, preventing rather than curing errors. This proactive approach reduces the time and resources needed for reactive correction.
Solution Approach 2:
The system dynamically adjusts reconciliation parameters and thresholds based on the specific context and severity of discrepancies. By changing parameters such as the threshold for flagging discrepancies or the weight given to different AI systems, the reconciliation process can be optimized to minimize time and resource consumption while maintaining high reliability standards.
Data Source
AI summary
A computer-implemented method of reconciling values of a feature, each value being provided by a different artificial intelligence (AI) system, by collecting logs from the different AI systems, each log including a value of the feature; identifying any discrepancy between the values. When there is any discrepancy, creating global information from the values, the global information taking into account some or all of the values. When the global information differs from the value of one of the AI systems, sending the global information to that AI system.