Explainable AI Knowledge Injection for Interpretable Model Training
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Solution Overview
Problem
Current machine learning systems lack the integration of human knowledge, leading to inefficiencies and difficulties in model interpretability, generalization, and potential biases, while expert systems require laborious rule encoding and are complex.
Innovation Solution
Implement human knowledge injection (HKI) into explainable artificial intelligence (XAI) systems using methods like expert system integration and human-assisted focusing, allowing human knowledge to be represented in symbolic logic and integrated with neural networks through techniques like gradient descent, ensuring traceability and accountability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If machine learning systems are used to automatically extract knowledge from data, then model performance and automation are improved, but interpretability and integration of human knowledge deteriorate
Solution Approach 1:
The patent merges symbolic logic systems (expert systems) with connectionist systems (neural networks) into a hybrid architecture. This allows the system to simultaneously perform automatic pattern recognition through neural networks while incorporating human-encoded logical rules and knowledge, thus maintaining both automation capabilities and human knowledge integration.
Solution Approach 2:
The patent introduces an intermediary layer that translates between neural network outputs and symbolic logic representations. This mediator enables the system to process automated machine learning results while filtering and integrating them with pre-existing human knowledge bases, preventing loss of structured human expertise.
2Loss of information
If expert systems are used to encode human knowledge in rules, then interpretability and human knowledge integration are improved, but system complexity and labor requirements worsen
Solution Approach 1:
The patent segments the knowledge representation into modular components: neural network modules for pattern recognition and symbolic logic modules for rule-based reasoning. This segmentation allows human knowledge to be encoded in manageable rule modules rather than monolithic complex systems, reducing overall system complexity while maintaining interpretability.
Solution Approach 2:
The patent creates a universal hybrid architecture that can handle both structured rule-based knowledge and unstructured data-driven patterns within a single system framework. This multi-functional system eliminates the need for separate expert systems for different tasks, reducing overall system complexity while maintaining human knowledge integration capabilities.
3Reliability
If deep learning models are used to achieve ground-breaking performance, then model accuracy is improved, but interpretability and accountability deteriorate
Solution Approach 1:
The patent implements feedback loops where the symbolic logic component continuously monitors and evaluates the neural network's decisions against encoded human knowledge and ethical rules. This feedback mechanism ensures that high-performance neural network predictions are validated against interpretable logical constraints, maintaining both accuracy and accountability.
Solution Approach 2:
The patent performs preliminary encoding of ethical guidelines, safety constraints, and domain expertise into symbolic logic rules before the neural network makes decisions. This preliminary action establishes interpretable boundaries and constraints that guide the high-performance neural network, ensuring accountability is built into the system architecture from the outset rather than added as an afterthought.
Data Source
AI summary
Human knowledge may be injected in an explainable AI system in order to improve the model's generalization error, model accuracy, interpretability of the model, avoid or eliminate bias, while providing a path towards the integration of connectionist systems with symbolic and causal logic in a combined AI system. Human knowledge injection may be implemented by harnessing the white-box nature of explainable/interpretable models. In one exemplary embodiment, a user applies intuition to model-specific cases or exceptions. In another embodiment, an explainable model may be embedded in workflow systems which enable users to apply pre-hoc and post-hoc operations. A third exemplary embodiment implements human-assisted focusing. An exemplary embodiment also presents a method to train and refine explainable or interpretable models without losing the injected knowledge defined by humans when applying gradient descent techniques. The white-box nature of explainable models allows for precise source attribution and traceability of knowledge incorporated into the model.