ML Output Evaluation Using Similarity Benchmarks
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Solution Overview
Problem
Existing machine learning models generate data items of varying quality, relevance, and accuracy, posing risks of deploying erroneous or biased outputs, which undermines the reliability and effectiveness of AI technologies, particularly in applications like financial crime detection where accurate reporting is critical.
Innovation Solution
A method for evaluating machine learning generated data items by computing similarity values between output and input data using vector embeddings and benchmarks, enabling reliable generation of suspicious activity reports and updating or retraining models based on these evaluations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning models generate data items automatically, then productivity is improved, but reliability deteriorates due to varying quality and accuracy of generated outputs
Solution Approach 1:
The patent implements an automated evaluation system that computes similarity values between generated output data items and reference input data items. This feedback mechanism compares the generated content against benchmarks and provides quality assessments, enabling the system to identify and correct errors in generated outputs, thereby maintaining reliability while preserving high productivity
Solution Approach 2:
The evaluation system operates autonomously to assess the quality of generated data items without requiring manual review. The system automatically computes similarity metrics, compares outputs against reference data, and identifies quality issues, enabling self-service quality control that maintains productivity while ensuring consistent reliability
2Reliability
If machine learning models are updated frequently to improve output quality, then reliability is improved, but loss of time increases due to model maintenance overhead
Solution Approach 1:
The patent establishes benchmark similarity values and reference data items in advance before model deployment. These pre-established benchmarks serve as the basis for automated evaluation, eliminating the need for time-consuming manual model updates. The system uses these preliminary references to continuously assess and maintain output quality, improving reliability without requiring frequent model retraining or updates
3Reliability
If comprehensive evaluation methods are implemented to ensure data quality, then reliability is improved, but device complexity increases
Solution Approach 1:
The patent replaces complex manual evaluation processes with automated computational similarity comparisons. Instead of requiring sophisticated human review systems or complex quality assurance mechanisms, the system uses algorithmic similarity computation between generated outputs and reference data, achieving comprehensive quality evaluation with simpler automated infrastructure
Data Source
AI summary
A system and method for evaluating machine learning generated data items, including: generating, by a machine learning model, an output data item based on an input data item, where the output item represents or corresponds to the input item (e.g., the output item is a textual description of a non-textual input item); computing a similarity value between the output item and the input item; and performing an exchange of data between remotely connected computer systems (such as, e.g., sending or transmitting the output item, or a computerized command to update or retrain the machine learning model) based on a comparison of the computed similarity value to a benchmark similarity value.