Automatic Theorem Solver for Explainable AI via Category Theory
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Solution Overview
Problem
Artificial Intelligence systems, particularly deep neural networks and predictive algorithms, lack explainability, making it difficult for traditional decision-makers to trust and adopt AI-based tools due to a lack of domain knowledge among engineers and statisticians, leading to hesitation in relying on AI-driven decisions.
Innovation Solution
An automatic theorem solver is developed to process data by converting it into morphisms within a category, using univariate and multivariate morphisms, chains of morphisms, and equations to provide explanations and answers to queries, enhancing the ability of engineers to reason about their products and improve them.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep neural networks and predictive algorithms are used to improve decision-making accuracy, then the accuracy of AI system is improved, but the explainability of the system deteriorates, making traditional decision-makers hesitant to adopt AI-based tools
Solution Approach 1:
The patent introduces category theory as an intermediary framework that bridges the gap between AI algorithms and human understanding. By translating AI decision processes into morphisms and chains within a category M, the system provides a mathematical structure that is both rigorous for maintaining accuracy and interpretable for human decision-makers, thus resolving the explainability issue without sacrificing measurement precision
Solution Approach 2:
The patent segments the AI decision-making process into discrete mathematical components: data structures are converted to morphisms, which are then organized into chains of morphisms. This segmentation allows each component to be analyzed and understood independently while maintaining the overall system's accuracy, making the black-box AI system transparent to traditional decision-makers
2Productivity
If engineers and statisticians are relied upon to develop AI algorithms, then the productivity of AI system development is improved, but the domain knowledge gap deteriorates, leading to solutions that prioritize engineering objectives over user understanding
Solution Approach 1:
The patent applies category theory, a universal mathematical framework, that can be applied across different domains and AI systems. This universal approach allows engineers to develop AI algorithms efficiently using standardized category-theoretic constructions while the resulting morphism-based representations remain interpretable by domain experts, thus maintaining both productivity and domain knowledge relevance without prioritizing one over the other
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.