Activation Profile Correlator for Neural Network Explainability
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Solution Overview
Problem
Current machine learning models lack transparency in explaining their inference processes, making it difficult for regulators and users to understand why certain decisions are made, particularly in applications like autonomous vehicles where predictability and fairness are crucial.
Innovation Solution
The implementation of Explainable Artificial Intelligence (XAI) technologies that allow for the determination and presentation of contextual dimensions relevant to the training data, enabling users to trace inference outputs back to the contextual dimensions of the training data, using techniques such as activation profile correlators and trusted execution environments to provide human-interpretable explanations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are trained on large amounts of data to improve accuracy, then model performance is improved, but transparency and explainability deteriorate
Solution Approach 1:
The patent segments the training data into different contexts (e.g., temporal context, spatial context, contextual dimensions) and analyzes each segment separately. This allows the system to maintain overall model accuracy while providing detailed explanations for specific inference decisions by examining relevant data segments without requiring full transparency of the entire training dataset.
Solution Approach 2:
The patent introduces an intermediary layer that connects the training data to the inference process. This intermediary mechanism traces activation profiles back to specific training samples and contextual dimensions, serving as a mediator that explains how training data influences model decisions without exposing the entire complex training process.
2Reliability
If training data is processed in detail to improve model context understanding, then inference accuracy is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent applies partial action by focusing computational resources only on the most relevant training samples and contextual dimensions that influence the inference decision. Instead of processing the entire training dataset in detail, the system identifies and analyzes only the necessary portions, reducing computational complexity while maintaining inference accuracy.
Solution Approach 2:
The patent performs preliminary actions by pre-processing and organizing training data into structured contexts and activation profiles before inference. This preliminary organization allows the system to quickly retrieve and analyze relevant information during inference without performing complex processing operations, thereby reducing real-time computational requirements.
3Loss of information
If contextual dimensions are extracted and analyzed from training data to improve explainability, then transparency is improved, but data processing requirements and system complexity increase
Solution Approach 1:
The patent extracts only the essential contextual dimensions and activation profile information from the training data, separating these key elements from the rest of the data. This extraction process maintains the necessary contextual information for explainability while reducing the overall data processing requirements by focusing only on the most relevant information.
Solution Approach 2:
The patent transforms the training data into a different dimensional representation by organizing it into contextual dimensions and activation profiles. This dimensional transformation allows the system to maintain rich contextual information in a structured format that is easier to process and analyze, reducing the complexity of data processing while improving explainability.
Data Source
AI summary
Logic may determine a specific performance of a neural network based on an event and may present the specific performance to provide a user with an explanation of the inference by a machine learning model such as a neural network. Logic may determine a first activation profile associated with the event, the first activation profile based on activation of nodes in one or more layers of the neural network during inference to generate an output. Logic may correlate the first activation profile against a second activation profile associated with a first training sample of training data. Logic may determine that the first training sample is associated with the event based on the correlation. Logic may output an indicator to identify the first training sample as being associated with the event.