AI Causal Explanation System Using Matrix Transformation
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Solution Overview
Problem
Artificial intelligence systems, due to their 'black box' nature, lack transparency in explaining their decision-making processes, making it difficult to identify and verify causal correlations, especially in critical operations where errors can lead to significant consequences, such as loss of life or financial devastation.
Innovation Solution
A method involving the formation of a matrix of correlations between input data and features, followed by transformation to eliminate non-causal correlations, resulting in a non-singular matrix that highlights potential causal correlations, allowing for the identification of causal indicators and improving the reliability of AI systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If AI systems use correlation-based processing to find patterns in training data, then they can effectively solve complex problems without complete understanding, but they become prone to unknown errors and non-causal correlations that cannot be easily verified
Solution Approach 1:
The patent introduces an intermediary explanation system that sits between the AI correlation processor and the user. This intermediary translates the black-box correlation decisions into human-understandable causal explanations, allowing verification without compromising the AI's problem-solving capability. The intermediary acts as a mediator that makes the invisible correlation process visible and verifiable.
Solution Approach 2:
The patent implements feedback mechanisms where the AI system's internal correlation states are fed back to users in an interpretable format. This allows users to verify whether the correlations match their understanding of causal relationships, creating a loop for validation and improvement of system reliability while maintaining adaptability.
2Productivity
If AI systems operate as black boxes to maintain simplicity and efficiency, then processing speed is improved, but transparency and explainability of decision-making processes are lost
Solution Approach 1:
The patent segments the AI system into two distinct parts: the correlation processor that maintains processing speed efficiency, and the explanation generator that provides transparency. This segmentation allows each component to optimize for its specific function - the correlation processor for speed and the explanation system for transparency - without compromising either.
Solution Approach 2:
The patent adds another dimension to the AI system by introducing an explanation layer that operates parallel to the correlation processing. This additional dimension provides transparency information without interfering with the primary processing function, effectively adding explainability as a separate dimension rather than compromising the existing processing efficiency.
3Reliability
If comprehensive testing of all outlier situations is performed to verify AI correlations, then reliability is improved, but the complexity and feasibility of verification becomes unmanageable
Solution Approach 1:
The patent applies preliminary action by generating explanations for AI decisions before actual outlier events occur. By having the explanation system ready and operational in advance, the system can immediately provide verifiable causal reasoning when outliers occur, rather than attempting to pre-test all possible outlier scenarios. This shifts the verification burden from comprehensive testing to on-demand explanation.
Solution Approach 2:
The patent enables the AI system to self-verify its correlations by generating its own explanations. Rather than requiring external comprehensive testing, the system serves itself by producing interpretable causal narratives that allow users to verify its reasoning. This self-service approach to verification dramatically reduces the complexity of the verification process while maintaining reliability.
Data Source
AI summary
A method and system for evaluating likely causal correlations between input data and output results of an artificial intelligence (AI) system identifies a plurality of features within each of a set of input data provided to the AI system. Each of the determined plurality of features is mapped to each input value of a set of input values and to the output result associated with each input value to form a matrix. From the matrix are removed at least some of the plurality of features that are not causal for the AI system determining the output results. Removal of at least some of the plurality of features that are not causal for the AI system determining the output results is repeated. When an end condition occurs, a resulting set of features that are each more likely to be causal than the plurality of features is provided as output causal data.