Tunable Bias Reduction Pipeline for AI Models
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Solution Overview
Problem
Existing approaches to reducing algorithmic bias in artificial intelligence models lack control over tunable and class-specific bias thresholds and penalization, often negatively impacting model performance and inference accuracy, especially when attempting to eliminate bias in applications like hiring personnel where some bias is acceptable.
Innovation Solution
A digital system and method for tunable bias reduction in AI models that determines a bias definition vector in a word embedding model, groups word vectors based on distance measurements, and applies a non-zero penalization factor to generate a debiased model that meets user-defined bias criteria, such as a target word ratio, while maintaining model performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If bias reduction approaches are applied to AI models, then bias in the model is reduced, but model performance and inference accuracy deteriorate
Solution Approach 1:
The patent applies parameter changes by introducing tunable bias thresholds and penalization factors that can be adjusted to control the balance between bias reduction and performance maintenance. The system allows users to define acceptable bias levels and adjusts the penalization intensity accordingly, transforming a binary bias elimination approach into a continuous parameter optimization problem.
Solution Approach 2:
The patent implements dynamics by making the bias reduction process adaptive and tunable rather than static. The penalization factor and bias thresholds can be dynamically adjusted based on user-defined criteria and application-specific requirements, allowing the system to adapt to different operational contexts and performance requirements.
2Object-affected harmful factors
If bias reduction approaches are applied to AI models, then bias is reduced, but control over class-specific bias thresholds is lost
Solution Approach 1:
The patent applies segmentation by breaking down the bias reduction process into class-specific components. Instead of applying a uniform bias reduction approach, the system identifies and processes different types of bias (e.g., gender bias, race bias) separately, allowing for class-specific threshold control and penalization strategies tailored to each bias type.
Solution Approach 2:
The patent implements local quality by allowing different bias thresholds and penalization factors for different classes or types of bias. The system enables users to define specific criteria for each bias category, applying appropriate reduction strategies locally rather than applying a one-size-fits-all approach.
3Object-affected harmful factors
If bias reduction approaches are applied to AI models, then bias is reduced, but model integrity and accuracy are compromised
Solution Approach 1:
The patent applies feedback by implementing iterative processes where the system monitors model performance and bias metrics, then adjusts the penalization factors and thresholds accordingly. This feedback loop allows the system to maintain inference accuracy while progressively reducing bias, making adjustments based on actual model behavior rather than assuming fixed outcomes.
Data Source
AI summary
Systems and methods may reduce bias in an artificial intelligence model. The system may receive word embedding model generated based on a corpus of words. The system may determine a bias definition vector in an embedding space of the word embedding model. The system may receive bias classification criteria. The bias classification criteria may include logic to group word vectors in the word embedding model based on a distance measurement from the bias definition vector. The system may identify, in the word embedding model, a first group of vectors and a second group of vectors based on the bias classification criteria and the bias definition vector. The system may generate a debiased artificial intelligence model. The debiased artificial intelligence model may include associations between words and metrics. The system may weight the metrics for the words associated with the first and second group of vectors with a non-zero penalization factor.