Synthetic Biased Test Sets for AI Fairness Configuration
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Solution Overview
Problem
AI models often produce biased predictions due to underlying biases in their training data, which can lead to unfair treatment of certain groups, and existing methods only identify bias when it exists in available training sets, lacking a proactive approach to detect and mitigate hidden biases.
Innovation Solution
The system proactively generates synthetic datasets with built-in bias to test AI models, allowing for the identification and mitigation of biases by exposing protected attributes to extreme scenarios, ensuring the model's robustness and fairness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional testing methods are used with available training sets, then the testing process is simple and straightforward, but bias in the model cannot be proactively detected or mitigated
Solution Approach 1:
The system performs preliminary actions by generating synthetic biased test data before actual model evaluation. The bias generation component creates test datasets with known bias characteristics, allowing the model to be tested against predetermined bias scenarios rather than waiting for bias to manifest in production
Solution Approach 2:
A bias generation component acts as an intermediary between the model evaluation component and the test data. This intermediary synthesizes biased test datasets with controlled bias levels, enabling systematic measurement of model robustness without requiring complex manual bias injection procedures
2Reliability
If synthetic biased test data is generated and used, then proactive bias detection and mitigation is enabled, but the testing process becomes more complex
Solution Approach 1:
The system implements feedback by measuring model performance on synthetic biased test data and using these measurements to adjust and improve model robustness. The bias measurement component provides quantitative feedback about bias levels, which feeds back into the model training and evaluation process
Solution Approach 2:
The system changes parameters by controlling the degree and type of bias injected into synthetic test datasets. The bias generation component can vary bias parameters such as bias magnitude, bias direction, and bias distribution, allowing systematic exploration of model behavior under different bias conditions
Data Source
AI summary
The invention is to utilize a process in which synthetic data sets are generated that contain intentional bias to run against an AI model. The purpose is to detect potential bias and if detected, perform bias mitigation procedure on the sensitive attributes associated with the bias. The embodiments are a construction component that constructs an initial artificial intelligence (AI) model using a structured data set with continuous, binary or multi-class prediction labels; a generation component that generates synthetic datasets from the training set wherein protected attributes are simulated; and an execution component that runs the initial model against synthetic biased testing data sets to gage robustness of the initial model by scoring the model and exposing protected attributes to target for bias mitigation.