Explainable AI Mechanism for Opaque Model Transparency
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Solution Overview
Problem
Current AI systems, particularly those using complex algorithms like Deep Learning, result in 'black/opaque boxes' where decisions are not transparent, making it difficult for humans to understand and trust AI outputs.
Innovation Solution
The development of novel Explainable AI (XAI) mechanisms that can be integrated with any AI system, allowing for the explanation of decisions, characterization of strengths and weaknesses, and translation of models into understandable explanations for end users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex algorithms like Deep Learning, Random Forests, and Support Vector Machines are used, then AI system performance and accuracy are improved, but transparency and interpretability of decisions deteriorate, creating 'black/opaque boxes'
Solution Approach 1:
The patent introduces an intermediary explanation layer that sits between the opaque AI model and the user. This layer includes natural language generation components and visualization tools that translate complex model decisions into human-understandable explanations, thereby maintaining high AI accuracy while improving transparency without modifying the core algorithm
Solution Approach 2:
The patent segments the AI system into distinct components: the opaque prediction model and the transparent explanation system. By separating these functions, the system can maintain high-performance complex algorithms while providing independent interpretability layers that explain decisions without interfering with the core model's accuracy
2Device complexity
If transparent models like decision trees and Bayesian networks are used, then interpretability is improved, but suitability for explaining AI decisions to lay persons deteriorates
Solution Approach 1:
The patent dynamically changes the parameters of explanations based on the user's knowledge level. For lay persons, it generates simple natural language explanations with minimal technical jargon. For experts, it provides detailed technical explanations including feature importance scores and model internals, thereby adapting the same transparent model to different user needs
3Productivity
If AI systems operate as black boxes, then productivity and automation are improved, but trust and understanding by human users deteriorate
Solution Approach 1:
The patent implements feedback loops where user reactions to explanations are captured and used to refine future explanations. The system monitors which explanations users find helpful and adjusts the explanation generation accordingly, creating a self-improving system that maintains high automation while progressively building user trust through increasingly effective explanations
Data Source
AI summary
A method of determining and explaining an artificial intelligence, AI, system employing an opaque model from a local or global point of view, the method comprising the steps of providing an input and a corresponding output of the opaque model; sampling the opaque model around the input to generate training data samples; performing feature selection to determine dominant features generating a Type-2 Fuzzy Logic Model, FLM; training the Type-2 FLM with the training data samples; and inputting the input into the Type-2 FLM to provide an explanation of the output from the opaque model.