Natural Language Explanations for Interpretable ML Predictions

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Solution Overview

Problem

Consumers are often unaware of the factors influencing predictions made by complex machine learning algorithms, lacking transparency and insight into how their attributes impact these predictions.

Innovation Solution

A system and method that utilizes an attribute impact analyzer to calculate the influence of user attributes on predicted values, generating natural language explanations to enhance user understanding.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If complex machine learning algorithms are used to make predictions, then prediction accuracy is improved, but transparency and user understanding deteriorate

Engineering Contradiction:

Improveprediction accuracyVSAvoidtransparency of prediction factors

Core Design Contradiction:

Measurement precisionVSLoss of information

Solution Approach 1:

The patent introduces an attribute impact analyzer as an intermediary component that bridges the complex machine learning model and the user. This analyzer calculates and explains the impact of each user attribute on the prediction, translating the opaque algorithmic decisions into understandable information without altering the original model's prediction accuracy.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Reliability

If complex algorithms are used in the prediction process, then prediction capability is improved, but user insight into influencing factors deteriorates

Engineering Contradiction:

Improveprediction capabilityVSAvoiduser insight into factors

Core Design Contradiction:

ReliabilityVSEase of operation

Solution Approach 1:

The patent segments the prediction process into two distinct parts: the complex machine learning model that generates predictions, and the attribute impact analyzer that explains them. This segmentation allows the system to maintain high prediction capability while separately providing user-friendly insights into which attributes influenced the prediction and how.

Inventive Principle:

Principle #1Segmentation

3Device complexity

If opaque prediction processes are used, then system complexity is reduced, but consumer awareness of used information deteriorates

Engineering Contradiction:

Improvesystem complexityVSAvoidconsumer awareness

Core Design Contradiction:

Device complexityVSLoss of information

Solution Approach 1:

The attribute impact analyzer serves as a mediator that adds transparency without significantly increasing system complexity. It processes the output of the existing machine learning model to generate explanations about which attributes were used and their impact, thereby maintaining consumer awareness while keeping the core prediction system relatively simple.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS12548083B1System and method for interpreting predictions from machine learning models using natural language

Publication Date: 2026.02.10 UIPCO LLC

AI summary

The embodiments provide a system and method for interpreting predictions from machine learning systems using natural language. The system includes a prediction module and an explanation module. The prediction module includes a machine learning model to make predictions for quantities such as recommended coverage amounts for insurance policies. The explanation module includes an impact analyzer that calculates impact values, which represent the degree of influence that each attribute has on predicted values. The explanation module also includes a natural language processing system for transforming generating natural language explanations indicating how the user attributes have influenced the predicted value.