Data Modification Operators for Fairer AI Model Inputs
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Solution Overview
Problem
Artificial intelligence and machine learning models often produce biased, inaccurate, or distorted outputs due to flawed training data, which can lead to unfair and unreliable decision-making.
Innovation Solution
A system that includes an output metric detector and data compensator to analyze and modify model inputs using data modification operators, ensuring compliance with policies and ethical standards by adjusting data with metatags, tokens, or lookup tables, and providing an explainability layer for auditing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data modification operators are applied to reduce bias in model outputs, then model fairness and reliability are improved, but device complexity increases
Solution Approach 1:
The patent introduces data modification operators as intermediary components that sit between the raw data input and the machine learning model. These operators act as mediators that transform biased data representations into adjusted data representations, reducing bias in model outputs without requiring changes to the model architecture itself. The operators include bias adjustment operators, data transformation operators, and weighting operators that can be applied selectively to different data features.
Solution Approach 2:
The patent modifies data parameters and characteristics through a set of transformation operators that adjust data representations. This includes changing data types, transforming data distributions, adjusting data weights, and modifying data features to reduce bias. The parameter changes are applied through configurable operators that can be tuned to specific bias reduction goals without retraining the underlying model.
2Reliability
If data modification operators are applied to reduce bias in model outputs, then model reliability is improved, but manufacturing precision (data accuracy) may worsen
Solution Approach 1:
The patent implements feedback mechanisms that monitor model outputs and data characteristics to automatically adjust data modification operators. The system evaluates the effectiveness of bias reduction and can fine-tune operator parameters based on observed performance, ensuring that data accuracy is maintained while bias reduction goals are achieved. Feedback loops allow continuous optimization of the balance between accuracy and fairness.
Solution Approach 2:
The data modification operators are designed to be dynamic and adaptable rather than static, allowing them to adjust their transformation parameters based on input data characteristics and model performance. This dynamic behavior enables the system to maintain data accuracy while reducing bias by automatically tuning operator strength and selection based on real-time conditions.
3Reliability
If data modification operators are applied to reduce bias, then output fairness is improved, but ease of operation decreases
Solution Approach 1:
The system performs self-service by automatically selecting, applying, and tuning data modification operators without requiring manual intervention. The automated bias reduction system monitors model outputs, identifies bias patterns, and applies appropriate operators autonomously. This self-service capability maintains ease of operation while achieving fair outputs, as users simply need to provide input data without configuring complex bias reduction parameters.
Data Source
AI summary
A system generates data modification operators that reduce bias or distortions in artificial intelligence (AI) models. The system uses a first artificial intelligence (AI) model to generate outputs based on a set of corresponding inputs to the first AI model. First measurement values of one or more model output metrics in the outputs generated by the first AI model are received. Based on the first measurement values, the system generates a set of data modification operators that specifies one or more operations for modifying inputs to a second AI model. Inputs to the second AI model can be modified using the set of data modification operators to generate a modified set of corresponding inputs. The second AI model can then be applied to the modified set of corresponding inputs to the second AI model.