Ranking Data Slices by Unique Information Contribution
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Solution Overview
Problem
Existing machine learning model testing methods struggle to effectively identify and prioritize data slices that highlight model weaknesses, due to the exponential number of possible data subsets and redundancy in information provided by overlapping slices, making it difficult for human inspectors to focus on critical areas for improvement.
Innovation Solution
A system and method that ranks data slices based on unique information contribution using a Shapley value approach, where each data slice is graded on its uniqueness with respect to others, enabling the identification of critical slices and explaining their significance, and aggregates these grades to provide an overall model performance assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data slices are created to identify model weaknesses, then model assessment quality is improved, but the number of data slices becomes exponentially large making inspection difficult
Solution Approach 1:
The patent extracts only the most critical and informative data slices by calculating a significance score for each slice based on error concentration and statistical metrics. Instead of presenting all exponentially many possible slices, the system extracts and prioritizes a manageable subset that provides the most valuable insights for model assessment.
Solution Approach 2:
The patent transforms the evaluation criteria by introducing composite significance scores that combine multiple parameters (error concentration, statistical significance, slice size) into a single ranking metric. This parameter transformation enables efficient prioritization of data slices without requiring manual inspection of all possible slices.
2Measurement precision
If multiple overlapping data slices are created, then comprehensive model coverage is improved, but redundancy in information increases making analysis difficult
Solution Approach 1:
The patent incorporates feedback mechanisms by calculating significance scores that account for the unique information contribution of each data slice. The scoring system provides feedback on which slices add valuable new information versus those that merely repeat patterns already captured by other slices, enabling intelligent deduplication of redundant information.
Solution Approach 2:
The patent applies local quality by assigning different weights and significance scores to different data slices based on their specific characteristics (error concentration, statistical significance, uniqueness). Rather than treating all slices uniformly, the system identifies and emphasizes locally important slices that provide unique insights into specific model weaknesses.
3Measurement precision
If manual inspection of data slices is performed, then detailed model analysis is achieved, but time consumption increases significantly
Solution Approach 1:
The patent performs preliminary action by automatically calculating significance scores and ranking data slices before human inspection. This pre-processing step prepares the data by identifying and prioritizing the most critical slices, so that human inspectors can focus their time on a pre-ranked, manageable subset rather than manually evaluating all possible slices from scratch.
Solution Approach 2:
The system performs self-service by automatically generating the significance scores and rankings without requiring manual human effort. The automated scoring mechanism independently evaluates each data slice's importance based on objective criteria (error rates, statistical metrics), freeing human inspectors from the time-consuming task of manual slice evaluation and prioritization.
Data Source
AI summary
An example system includes a processor to receive a test set, data slices, and a measure of interest. The processor can rank the data slices based on the test set, the data slices, and the set of measures of interest. The test set includes data points from the same feature space used to train a machine learning model. Each data slice is ranked according to generated slice grades representing unique information contribution of each data slice to the measure of interest with respect to the other data slices. The processor can then present the ranked data slices.