Interactive Visual Framework for Transparent Algorithmic Decisions
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Solution Overview
Problem
Current machine learning models operate in a monolithic and black box fashion, lacking transparency in pattern recognition and decision-making, necessitating external applications to consolidate decisions and accommodate evolving business conditions, which complicates compliance and ethical policy implementation.
Innovation Solution
A method and system for generating an interactive visual framework that utilizes multiple machine learning algorithms to identify patterns, create metadata models, and generate a visual chart with nodes and connections, allowing for real-time review and revision of decision-making processes, incorporating compliance and operational guardrails.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple machine learning models are used to solve complex problems, then the problem-solving capability is improved, but the lack of transparency and the need for external applications to consolidate decisions increases system complexity
Solution Approach 1:
The patent combines multiple machine learning models and their decision-making processes into a single visual framework that displays all models, their predictions, and interrelationships in one integrated interface. This merging eliminates the need for separate external applications to consolidate decisions, reducing system complexity while maintaining the ability to handle complex problems through multiple models.
Solution Approach 2:
The visual framework acts as an intermediary layer between multiple machine learning models and end users or downstream systems. It consolidates and visualizes decisions from multiple models, providing transparency and eliminating the need for additional external applications, thus reducing overall system complexity.
2Ease of manufacture
If machine learning models operate in black box fashion, then model training and deployment is simplified, but transparency and ability to examine decision-making is reduced
Solution Approach 1:
The visual framework uses visual indicators such as color coding, node styling, and connection highlighting to represent different aspects of model decision-making. This visual encoding makes the internal workings of black box models transparent and examinable while maintaining deployment simplicity, as the framework automatically generates these visual representations without complicating the model training process.
3Reliability
If external applications are built to consolidate decisions and apply compliance policies, then compliance and ethical requirements are met, but the complexity of accommodating evolving business conditions increases
Solution Approach 1:
The visual framework is designed to dynamically adapt to evolving business conditions by automatically updating visual representations when models are retrained or new models are added. Compliance policies and operational guardrails can be adjusted within the framework without requiring extensive reconfiguration, thus maintaining both reliability and adaptability.
Solution Approach 2:
The visual framework serves multiple functions including displaying model predictions, visualizing decision pathways, enforcing compliance policies, and accommodating evolving business conditions through a single unified interface. This multi-functionality eliminates the need for separate external applications, maintaining compliance assurance while improving adaptability.
Data Source
AI summary
A method for generating an interactive visual framework to review decision making of machine learning model analysis. The method can include receiving a set of data related to a scenario, with the set of data having attributes associated with a plurality of parameters. A machine learning algorithm can identify patterns in the attributes, including mapping subsets of the attributes to one or more outcomes of the scenario. The machine learning algorithm can generate a machine learning model based on the identified patterns. The machine learning model can include sets of metadata representing an outcome of the scenario. The machine learning model can identify correlations between the sets of metadata. A node for each metadata and a connection between each node having correlated metadata can be generated. The interactive visual framework can include a chart, with each node positioned on the chart based on the metadata.