Causal Information Preservation in Machine Learning Models
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Solution Overview
Problem
Machine learning models, particularly non-linear models, obscure the decision-making process, making it difficult to determine the significance of various factors contributing to a decision, which is problematic in applications like credit assessments where transparency is legally required.
Innovation Solution
The method involves computing relative weights for each feature in the machine learning model to measure the influence of each variable on the outcome, allowing for the preservation of causal information and transparent decision-making processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex non-linear machine learning models are used to improve decision accuracy, then prediction accuracy is improved, but interpretability and transparency of the decision-making process deteriorate
Solution Approach 1:
The patent introduces an intermediary system that processes the output of complex machine learning models and generates human-readable explanations. This intermediary layer translates the opaque decisions of non-linear models into interpretable causal narratives, allowing the system to maintain high prediction accuracy while restoring transparency through a mediating explanation generation component.
Solution Approach 2:
The system dynamically adjusts parameters such as explanation depth, detail level, and causal pathway selection based on the specific model output and context. By changing these parameters, the system can optimize between providing comprehensive causal explanations versus more concise interpretations, thereby recovering lost causal information at different operational states.
2Reliability
If multiple factors are considered in machine learning models to improve decision quality, then decision quality is improved, but ability to ascertain the influence of individual factors deteriorates
Solution Approach 1:
The patent segments the complex decision-making process into distinct causal pathways, each associated with specific input factors. By dividing the overall decision into separable causal chains, the system can trace and measure the influence of individual factors through their respective pathways, making it possible to ascertain factor importance even when multiple factors are considered together.
Solution Approach 2:
The system constructs a composite explanation structure that integrates multiple causal pathways and factor influences into a unified interpretive framework. This composite structure combines information from various factors while maintaining their individual identities and relative influences, allowing the system to preserve factor distinguishability despite considering multiple factors in the decision process.
Data Source
AI summary
Machine learning models are powerful artificial intelligence tools that can make determinations based on a variety of factors. Unlike a simple linear model, however, determining the contribution of each variable to the outcome of a machine learning model is a challenging task. It may be unclear which factors contributed heavily toward a particular outcome of the machine learning model and which factors did not have a major effect on the outcome. Being able to accurately determine the underlying causative factors for a machine learning-based decision, however, can be important in several contexts. The present disclosure describes techniques that allow for training and use of non-linear machine learning models, while also preserving causal information for outputs of the models. Relative weight calculations for machine learning model variables can be used to accomplish this, in various embodiments.