Structural Causal Model for Confounding Bias Identification
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Solution Overview
Problem
Existing methods for estimating bias in machine learning models are ineffective when the ground truth is not known, particularly in subjective domains like art, where data and models may contain hidden or subconscious biases, leading to non-representative conclusions and unfair treatment in applications such as creditworthiness, healthcare, and judiciary.
Innovation Solution
A method that utilizes expert knowledge to generate a structural causal model, identifies confounding factors, and estimates bias by comparing perceived and observed causal effects, allowing for adjustment of machine learning models to reduce confounding bias.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional bias estimation methods are used, then the process is simple, but they are ineffective when ground truth is not known
Solution Approach 1:
The patent introduces expert knowledge as an intermediary element that mediates between the machine learning model and the bias estimation process. Experts provide causal relationships and domain-specific insights that serve as a bridge when ground truth is unavailable, enabling reliable bias detection through their specialized understanding rather than direct measurement against known truths.
Solution Approach 2:
The patent performs preliminary actions by collecting and structuring expert knowledge before the actual bias estimation process. Experts are interviewed and their insights are encoded into the analysis framework in advance, creating a preparatory knowledge base that enables subsequent bias detection without requiring ground truth data during the estimation phase.
2Reliability
If expert knowledge is incorporated to improve bias detection, then reliability improves, but the process becomes more complex
Solution Approach 1:
The patent segments the complex task of bias detection into distinct components: expert knowledge collection, structural causal model construction, confounding factor identification, and bias estimation. Each component handles a specific aspect of the problem, making the overall complex process more manageable and systematic rather than attempting to solve all aspects simultaneously.
Solution Approach 2:
The structural causal model serves as an intermediary framework that organizes expert knowledge into a structured format. This model acts as a bridge between unstructured expert insights and the formal bias estimation process, translating qualitative expert knowledge into a structured representation that can be systematically analyzed for confounding factors.
3Manufacturing precision
If confounding factors are identified and bias is reduced, then model fairness improves, but the adjustment process increases complexity
Solution Approach 1:
The patent extracts confounding factors from the machine learning model through systematic analysis of the structural causal model. By identifying and separating these confounding variables from the core model relationships, the method enables targeted adjustments that remove sources of bias without requiring complete model reconstruction, thus improving fairness while managing complexity.
Data Source
AI summary
A method may include obtaining a machine-learning model trained with respect to a subject. The machine-learning model may be based on a plurality of factors that correspond to the subject. The method may include obtaining human provided information regarding the subject. The expert information may indicate relationships between the plurality of factors with respect to how the plurality of factors affect each other. The method may include generating a structural causal model that represents the relationships between the plurality of factors based on the expert information. The method may include identifying, as a confounding factor and based on the structural causal model, a factor of the plurality of factors that causes a confounding bias in the machine-learning model. The method may include estimating the confounding bias based on the identified confounding factor.