AI Model Bias Analysis via Intermediary Assessment Layer

The patent introduces an intermediary analysis system that sits between the machine learning model and the decision-making process. This system includes processors that assess model data, identify fields, analyze relationships, and generate notifications about potential biases. The intermediary layer enables automated decision-making to continue while simultaneously monitoring and detecting biases that may affect protected groups, thus resolving the contradiction between productivity improvement and bias prevention.

The patent segments the model analysis process into distinct functional modules: receiving model data, assessing fields, identifying related fields, analyzing relationships, and generating notifications. Each module performs a specific task in the bias detection process, making the overall complex system more manageable and maintainable. The segmentation allows the system to achieve comprehensive bias detection while organizing complexity into discrete, manageable components.

The patent implements preliminary action by automatically identifying and analyzing related fields before conducting the full bias assessment. The system receives model data, pre-identifies relevant fields and their relationships, and prepares the analysis framework in advance. This preliminary structuring of the analysis allows for more precise bias detection across multiple fields while reducing the overall time required, as the foundational work is completed before the actual bias measurement begins.