Automatic Theorem Solver Using Morphism Chains for Explainable AI
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Solution Overview
Problem
Artificial Intelligence systems, particularly deep neural nets, lack explainability, leading to hesitation among traditional decision-makers to adopt AI-based predictive and directive tools due to a lack of domain knowledge among engineers and statisticians.
Innovation Solution
An automatic theorem solver that processes data to generate chains of morphisms and equations, using a category-based approach to provide answers to queries, enhancing the understanding and decision-making capabilities of engineers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If deep neural nets and predictive algorithms are used to improve AI accuracy and performance, then the system becomes a black box that lacks explainability, making traditional decision-makers hesitant to adopt the system
Solution Approach 1:
The patent introduces an automatic theorem prover as an intermediary component that generates formal proofs explaining the reasoning behind AI predictions. This mediator translates the black-box decisions into verifiable logical chains, allowing traditional decision-makers to understand and trust AI recommendations without sacrificing prediction accuracy
Solution Approach 2:
The patent segments the AI decision-making process into distinct components: the predictive algorithm generates predictions, while the automatic theorem prover separately generates formal explanations. This segmentation allows the system to maintain high accuracy through advanced algorithms while providing separate, interpretable proof traces for each prediction
2Productivity
If engineers and statisticians develop AI algorithms to improve predictive capabilities, then the system performance improves, but the lack of domain knowledge among developers reduces user trust and adoption
Solution Approach 1:
The automatic theorem prover operates autonomously to generate formal proofs without requiring domain experts to manually create explanations. The system self-services by automatically translating AI predictions into verifiable logical arguments, eliminating the need for developers to possess both algorithmic and domain expertise simultaneously
3Productivity
If traditional decision-makers fully adopt AI-based tools, then decision-making efficiency increases, but the loss of human judgment and domain expertise reduces decision quality
Solution Approach 1:
The patent implements a feedback mechanism where the automatic theorem prover provides formal proofs back to the decision-making process. These proofs serve as verifiable feedback that confirms the logical validity of AI recommendations, allowing traditional decision-makers to maintain human judgment while benefiting from AI efficiency
Data Source
AI summary
Some embodiments of the present disclosure provide a manner for an automatic theorem solver to answer a query. Ahead of time, data that supports columns is received. The data is converted to a data structure. Sets of univariate and multivariate morphisms are then determined and the numbers of morphisms in the sets may be reduced in accordance with various metrics. Additionally, the morphisms may be used to generate chains of morphisms. A plurality of equations may be selected for a category. Upon receiving the morphisms, chains of morphisms and selected equations, the automatic theorem solver may be ready to receive a query. The automatic theorem solver may then determine an answer to the query and present the answer.