Multi-Modal Neural Networks for Bias-Aware Synthetic Data Selection
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Solution Overview
Problem
Synthetic data generation in machine learning introduces bias and misrepresentation issues, failing to adapt dynamically to evolving societal biases and ensuring fair decision-making.
Innovation Solution
A multi-modal neural network framework with smart contracts on a blockchain to evaluate and select synthetic data sources based on predefined rules, continuously monitoring variance and drift to ensure compliance with real-world data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If synthetic data is generated to fulfill machine learning training requirements, then data availability and productivity are improved, but bias and misrepresentation issues worsen
Solution Approach 1:
The patent implements a feedback mechanism where the neural network continuously evaluates synthetic data against real-world data characteristics and bias criteria. The system monitors data quality metrics and adjusts generation parameters dynamically to reduce bias while maintaining productivity. This closed-loop feedback ensures that synthetic data meets reliability standards without sacrificing generation efficiency.
Solution Approach 2:
The patent introduces an intermediary neural network layer that acts as a mediator between synthetic data generation and machine learning training. This intermediary evaluates and filters synthetic data to eliminate biases before feeding it to the training process, thus resolving the contradiction between maintaining data availability and ensuring reliability.
2Productivity
If synthetic data generation processes are used to meet training data demands, then productivity increases, but the ability to capture evolving societal biases deteriorates
Solution Approach 1:
The patent employs dynamic evaluation criteria that adapt to evolving societal biases. The neural network's bias detection mechanisms are continuously updated to recognize new forms of bias as they emerge in society. This dynamic adaptability allows the system to maintain high productivity while staying current with changing bias patterns.
Solution Approach 2:
The system performs preliminary evaluation of synthetic data against anticipated bias patterns before the biases fully manifest in the training process. By proactively identifying and correcting potential bias issues, the system maintains both productivity and adaptability to evolving societal norms.
3Reliability
If multiple rules and neural networks are implemented to evaluate synthetic data, then bias mitigation improves, but device complexity increases
Solution Approach 1:
The patent segments the bias mitigation process into multiple specialized neural networks, each responsible for evaluating specific types of biases. This segmentation allows for targeted and efficient bias detection without requiring a single overly complex system. Each neural network module can be independently optimized and managed.
Solution Approach 2:
The patent designs universal neural network components that can evaluate multiple types of biases across different data contexts. These multi-functional evaluation modules reduce overall system complexity by avoiding the need for separate specialized systems for each bias type, while still maintaining comprehensive bias mitigation capabilities.
Data Source
AI summary
Systems, computer program products, and methods are described herein for targeted synthetic data extraction and analysis via a multi-modal neural network. The present disclosure includes transmitting a training data request for synthetic data with a requirements payload having a plurality of rules via a smart contract. Upon a condition where a first rule is satisfied, first compliant synthetic data may be input to a first primary neural network. Upon a condition where a second rule is satisfied, second compliant synthetic data may be input to a second primary neural network. A first secondary neural network may receive the outputs of the first and second primary neural networks to determine one or more aggregate preferred synthetic data sources.