Explanation Profiles for Low-Latency ML Model Interpretability
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing machine learning (ML) models, particularly complex ones, are challenging to interpret due to their increasing complexity, and current explainability techniques fail to provide clear explanations in dynamic, evolving environments, especially in high-dimensional datasets with diverse data types, leading to issues like alert fatigue and reduced trust in ML decisions.
Innovation Solution
The use of dynamically selected explanation profiles that generate local explanations by precomputing statistical summaries of log entries, incorporating temporal and spatial contexts, and providing human-friendly outputs, adaptable to various ML architectures and domains, including cybersecurity applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If complex ML models are used to solve complex problems, then problem-solving capability is improved, but model interpretability deteriorates
Solution Approach 1:
The patent introduces explanation profiles as an intermediary layer between the complex ML model and the user. These profiles pre-compute and store contextual information (spatial, temporal, statistical) that mediates the interpretation process, allowing complex model outputs to be explained without simplifying the model itself.
Solution Approach 2:
The system performs preliminary actions by pre-computing statistical summaries, spatial relationships, and temporal patterns during offline processing. This preparation work is stored in explanation profiles, enabling fast online explanation generation without requiring complex computations during inference.
2Loss of information
If traditional MLX techniques are used to explain model predictions, then local explainability is improved, but computational overhead and latency increase
Solution Approach 1:
The patent pre-computes explanation data including statistical summaries, spatial contexts, and temporal patterns during offline processing. These pre-computed explanations are stored in profiles and retrieved during online operations, eliminating the need for expensive real-time computations and reducing latency significantly.
Solution Approach 2:
The system extracts only the essential explanatory features and contextual information needed for interpretation, separating them from the full model computation. By extracting and storing only relevant explanation data in profiles, the system avoids computing unnecessary information during online operations.
3Stability of the object's composition
If static explanation methods are used in stationary environments, then explanation consistency is improved, but adaptability to dynamic environments deteriorates
Solution Approach 1:
The patent implements dynamic explanation profiles that can be updated and adapted to changing environments. The system continuously learns from new data and updates the stored statistical summaries, spatial relationships, and temporal patterns, allowing explanations to remain consistent with the current state of the system while adapting to dynamic conditions.
Solution Approach 2:
The explanation profile framework is designed to be universal and applicable across different domains and ML models. The same profile structure can accommodate various types of contextual information (spatial, temporal, statistical) and work with different model architectures, providing both consistency in methodology and adaptability to diverse applications.
4Loss of information
If comprehensive contextual information is included in explanations, then explanation completeness is improved, but information overload and alert fatigue increase
Solution Approach 1:
The patent applies local quality by providing different levels and types of explanation information tailored to specific needs and contexts. Rather than uniformly providing all possible contextual information, the system selectively presents relevant explanations based on the specific prediction, user needs, and situational context, making explanations complete where needed and concise elsewhere.
Solution Approach 2:
The system implements partial action by providing only the necessary portion of contextual information required for understanding specific predictions. The explanation profiles store comprehensive data but selectively retrieve and present only the relevant subset needed for each explanation, avoiding information overload while maintaining completeness for the specific case.
Data Source
AI summary
In an embodiment, a computer stores, in memory or storage, many explanation profiles, many log entries, and definitions of many features that log entries contain. Some features may contain a logic statement such as a database query, and these are specially aggregated based on similarity. Based on the entity specified by an explanation profile, statistics are materialized for some or all features. Statistics calculation may be based on scheduled batches of log entries or a stream of live log entries. At runtime, an inference that is based on a new log entry is received. Based on an entity specified in the new log entry, a particular explanation profile is dynamically selected. Based on the new log entry and statistics of features for the selected explanation profile, a local explanation of the inference is generated. In an embodiment, an explanation text template is used to generate the local explanation.