Predictive Classifier Narrative Generation for Regulatory Explainability
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Solution Overview
Problem
Existing predictive ML models lack the capability to generate narratives that provide transparent and regulatory-compliant explanations for their decision-making processes, particularly in sectors like finance and healthcare, where regulatory bodies require detailed insights into the logic behind automated decisions.
Innovation Solution
A narrative generation platform that utilizes ML techniques to convert predictive classifier outputs into human-readable narratives, incorporating temporal and quantitative information, population-wide and cluster-wide statistics, and user feedback to ensure transparency and compliance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If predictive ML models provide detailed explanations for classifier outputs, then regulatory compliance and decision-making transparency are improved, but the complexity of the system increases
Solution Approach 1:
The system segments the explanation generation process into distinct modular components: a narrative generation module that creates human-readable explanations, a calculation engine that computes statistical metrics, and a data processing layer that prepares classifier outputs. This modular segmentation allows each component to handle specific tasks independently, improving regulatory compliance while managing system complexity through clear separation of concerns.
Solution Approach 2:
The narrative generation module acts as an intermediary between the predictive ML model and regulatory stakeholders. It translates complex classifier outputs into human-readable narratives that satisfy regulatory requirements without exposing the underlying system complexity. This intermediary layer provides the necessary explanations while shielding the complexity of the ML model from end users and regulators.
2Loss of information
If the system generates comprehensive narratives with temporal and quantitative information, then explanation quality is improved, but the processing time and computational resources increase
Solution Approach 1:
The calculation engine pre-computes statistical metrics such as population-wide statistics, cluster-wide statistics, and temporal patterns before narrative generation. By performing these calculations in advance and caching results, the system reduces processing time during actual explanation generation while maintaining comprehensive information quality in the final narratives.
Solution Approach 2:
The system implements selective narrative generation that provides partial explanations based on the specific regulatory context and user needs. Rather than always generating complete comprehensive narratives, the system can provide targeted explanations focusing on the most relevant temporal and quantitative information, reducing processing time while maintaining sufficient explanation quality for different use cases.
3Loss of information
If the narrative includes population-wide and cluster-wide statistics, then the depth of insight is improved, but the data processing complexity increases
Solution Approach 1:
The data processing pipeline is segmented into distinct stages: data collection from multiple sources, population-wide statistical analysis, cluster-wide statistical analysis, and narrative integration. Each stage handles specific data processing tasks independently, allowing the system to compute comprehensive statistics without overwhelming complexity. The segmentation enables parallel processing and independent optimization of each statistical analysis component.
Solution Approach 2:
The calculation engine is designed as a universal multi-functional component that can compute various statistical metrics (population-wide statistics, cluster-wide statistics, temporal patterns, quantitative measures) using a unified framework. This multi-functionality reduces overall data processing complexity by reusing the same engine for different statistical analyses rather than maintaining separate processing systems for each type of insight.
Data Source
AI summary
A computer-implemented method, comprising: selecting, from a list of available functions by one or more processors, a function based on an output of a predicative classifier; retrieving, by the one or more processors, a dataset relevant to the selected function, wherein the dataset is a time series dataset; analyzing, in accordance with the selected function by a calculation engine, the dataset to derive temporal information and quantitative information associated with the dataset; and generating, by the one or more processors, a narrative for the output of the predicative classifier based on the temporal information and the quantitative information.