Black Box Algorithm Explanation System
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Machine learning systems, particularly black box algorithms, lack transparency in their decision-making processes, making it difficult for human users to understand their outputs and reducing trust, and complicating troubleshooting when unexpected or erroneous results occur.
Innovation Solution
An automated system that generates natural language explanations for black box algorithm outputs by performing regression analysis, determining decision trees, and identifying relevant attributes and their effects, allowing for real-time explanations of categorizations and decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If black box algorithms are used for machine learning predictions, then prediction accuracy and automation are improved, but transparency and understandability of decision-making processes deteriorate
Solution Approach 1:
The patent introduces an explanation generation system as an intermediary component that sits between the black box algorithm and the user. This mediator takes the input data and algorithm outputs, performs regression analysis and decision tree generation, and produces human-understandable explanations without altering the original algorithm's predictive functionality.
Solution Approach 2:
The patent segments the explanation generation process into distinct analytical components: regression analysis for coefficient determination, decision tree construction for path identification, and natural language generation for explanation formulation. This segmentation allows each component to address specific aspects of transparency while maintaining overall system integration.
2Extent of automation
If black box algorithms are used, then automation extent is improved, but ease of operation and troubleshooting deteriorate
Solution Approach 1:
The explanation system serves as a mediator that translates automated algorithmic decisions into human-readable formats. It processes the automated outputs through regression analysis and decision tree methods to generate explanations that maintain the automation benefits while improving user comprehension and operational ease.
Solution Approach 2:
The patent transforms the output parameters from raw algorithmic predictions into explained decisions with identified relevant attributes and their effects. By changing the parameter representation from opaque numerical outputs to structured explanations with natural language descriptions, the system maintains automation while improving ease of operation.
3Productivity
If black box algorithms are used, then productivity is improved, but ease of repair and troubleshooting deteriorate
Solution Approach 1:
The patent performs preliminary analysis by generating decision trees and identifying relevant attributes before final decisions are made. This preliminary action creates a roadmap of the decision-making process that can be used for troubleshooting, allowing users to understand which attributes influenced decisions and how the algorithm reached its conclusions.
Solution Approach 2:
The explanation system provides feedback about the algorithm's decision-making process by identifying relevant attributes and their effects on outcomes. This feedback mechanism enables troubleshooting by showing users which input attributes most influenced the prediction, allowing for targeted verification and error correction.
Data Source
AI summary
Systems, apparatuses, and methods are provided herein for providing natural language explanation to black-box algorithm generated outcome. The system is configured to determine a regression coefficient for each of the plurality of attributes based regression analysis, determine a decision tree based on the input data and the output data and a decision path of a select data item in the decision tree, generate natural language explanation of a categorization of the select data item based on the relevant attributes and regression coefficients associated with each of the relevant attributes, wherein the natural language explanation identifies at least one relevant attribute and an effect of the at least one relevant attribute of the data item on the categorization, and transmit to a user interface device for display, the categorization of the select data item along with the natural language explanation of the categorization of the select data.