AI/ML Decision Rectification Using Regional Training Data Feedback
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Solution Overview
Problem
AI/ML models often suffer from bias due to inadequate training data, leading to unintended decision outcomes, which can be exacerbated by regional differences and the evolving nature of training data over time.
Innovation Solution
An intelligent AI/ML model decisioning and rectification system that allows real-time user interaction to challenge biased decisions, utilizing a distributed ledger network and quantum swarm intelligence to fetch and synthesize relevant training data from a network of similar models, ensuring data quality and minimizing bias.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If AI/ML models are trained with inadequate training data, then model training speed is improved, but decision accuracy and fairness deteriorate due to model bias
Solution Approach 1:
The system performs preliminary actions by proactively identifying biased decisions before they are finalized, using a bias detection module that analyzes model outputs in real-time. When bias is detected, the system preemptively triggers a retraining process with corrected training data, preventing biased decisions from being executed. This preliminary intervention resolves the contradiction by ensuring accuracy improvements occur before productivity concerns can manifest.
Solution Approach 2:
The system implements a continuous feedback loop where model decisions are monitored, bias is detected, and corrective feedback is provided back to the training data. The bias detection module feeds information about problematic decisions to the data synthesis engine, which generates corrected training examples. This feedback mechanism ensures that decision accuracy is continuously improved without requiring complete retraining, thus maintaining productivity while eliminating bias.
2Measurement precision
If AI/ML models are retrained frequently to correct bias, then decision accuracy is improved, but system complexity and computational resources increase
Solution Approach 1:
Instead of performing complete model retraining whenever bias is detected, the system applies partial action by selectively updating only the specific portions of training data that contain biases. The data synthesis engine generates targeted corrections for identified biased decisions rather than retraining the entire model from scratch. This partial update approach maintains decision accuracy while significantly reducing system complexity and computational overhead.
Solution Approach 2:
The system changes parameters by adjusting the training data composition dynamically rather than changing the model architecture or training methodology. When bias is detected, the system modifies the training data parameters (adding diverse examples, removing biased patterns) and retrains only the necessary components. This parameter-based approach simplifies the overall system while maintaining the ability to correct bias effectively.
3Reliability
If real-time bias correction is implemented, then fairness and decision quality are improved, but processing time and operational speed decrease
Solution Approach 1:
The system implements periodic action by conducting bias detection and correction at strategically determined intervals rather than continuously for every single decision. The monitoring module samples model outputs at regular intervals and triggers retraining only when bias thresholds are exceeded. This periodic approach maintains fairness and decision quality while minimizing the time loss associated with frequent corrections, allowing the system to operate at full speed during normal periods.
Solution Approach 2:
The system applies skipping by rapidly processing bias detection and correction when triggered, using optimized algorithms that can identify and fix biased decisions in minimal time. The data synthesis engine and model retraining process are designed to complete quickly, allowing the system to rush through the correction process and return to normal high-speed operation. This rushing through of corrections minimizes the impact on overall processing time while maintaining fairness.
Data Source
AI summary
An artificial intelligence/machine learning (AI./ML) model decisioning and rectification system is configured to allow a user to challenge decisions output by systems processing AI/ML models. The user interacts with the AI/ML model decisioning and rectification system, directly or via another computing device, to trigger AI/ML training data acquisition from multiple regional data stores processing the AI/ML model. A data acquisition module is composite apparatus that intelligently fetches training data sets applicable to the particular AI/ML model in a distributed network and initiates intelligent retraining of the AI/ML model based on an aggregated regional training data set.