Data Subject Assessment Service for AI Risk Analysis
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Solution Overview
Problem
Current artificial intelligence platforms face challenges in achieving accurate natural language understanding due to inaccuracies in input data, leading to less-than-satisfactory outcomes in natural language processing and understanding tasks.
Innovation Solution
A data subject assessment system is developed, which includes defining a data subject, creating a project, configuring it with AI models and risk levels, and running assessments using text mining operations and rule applications to produce metadata and actionable insights, allowing for more precise identification of risks and relationships within documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional NLP capabilities (concept extraction, named entity extraction, text classification) are used, then basic text processing can be performed, but accurate natural language understanding and granular data subject assessment cannot be achieved
Solution Approach 1:
The system segments the NLU process into distinct functional modules: concept extraction module, named entity extraction module, text classification module, and sentiment analysis module. Each module handles a specific aspect of text analysis and produces structured output that feeds into the next module, enabling granular assessment while maintaining manageable system complexity through modular architecture
Solution Approach 2:
The system merges multiple NLP capabilities (concept extraction, named entity extraction, text classification, sentiment analysis) into an integrated NLU platform that processes text through a unified pipeline. The modules work together synergistically, with each module's output contributing to the overall assessment accuracy, achieving comprehensive understanding that none of the individual modules could achieve alone
2Measurement precision
If basic NLP modules are deployed, then general text processing is possible, but granular identification of data subjects and precise risk assessment cannot be achieved
Solution Approach 1:
The system performs preliminary text processing through concept extraction and named entity extraction modules before applying text classification and sentiment analysis. This staged approach pre-processes the text into structured formats with extracted entities and concepts, making subsequent classification and assessment operations more efficient and accurate without requiring reprocessing of raw text
Solution Approach 2:
The system introduces intermediary data structures (extracted concepts, named entities, and classified tags) that mediate between raw text input and final assessment output. These intermediaries transform unstructured text into organized information that can be efficiently processed by subsequent modules, improving both accuracy and processing efficiency
Data Source
AI summary
Responsive to user interaction, a data subject assessment service, hosted on an artificial intelligence (AI) platform operating in a cloud computing environment, is operable to define a data subject, create and configure a data subject project, and add the data subject to the data subject project. The data subject project is associated with AI models, each of which models a risk having a user-adjustable risk level. The data subject project thus configured and/or customized, for instance, with a custom rule, can be run on a collection of documents to assess the data subject through data subject assessment operations. Data subject assessment results thus produced can be searched for data subject relationships, using metadata from the data subject assessment operations. This fine-tunes the data subject assessment results and produces more granular, more precise results, based on which a report can be viewed and/or generated.