Visual Analytics for Explainable Data Slicing
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Solution Overview
Problem
Current data slice-finding methods for machine learning model validation require extensive metadata, which is costly and labor-intensive to collect, and also require non-trivial efforts to interpret the root causes of under-performing data slices.
Innovation Solution
A novel human-in-the-loop visual analytics workflow that uses pixel attributions to create interpretable features without any additional metadata, allowing for the identification and annotation of problematic data slices and the mitigation of slice errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If extensive metadata is collected for data slice-finding, then model validation accuracy is improved, but human effort and cost increase significantly
Solution Approach 1:
The system uses the model's own predictions and feature vectors to automatically identify and group problematic data slices, eliminating the need for manual metadata collection. The model validates itself by generating feature representations that reveal performance patterns across different data subsets.
Solution Approach 2:
Manual metadata collection and analysis is replaced with automated computational processes. The system uses machine learning models to generate feature vectors and perform clustering algorithms to identify data slices, substituting human analytical work with automated computational methods.
2Loss of information
If metadata is collected using multi-modal pre-trained vision-language models, then data slice interpretability is improved, but computational cost and complexity increase
Solution Approach 1:
The system extracts only the necessary feature vectors from the model's internal representations that are relevant to identifying problematic data slices. Instead of using complex multi-modal models to generate all possible metadata, it selectively extracts feature representations that directly indicate model performance issues.
Solution Approach 2:
The feature vectors extracted from the model serve multiple purposes: they are used for both model validation and for identifying problematic data slices. The same computational infrastructure that trains the model is leveraged to perform validation, eliminating the need for separate complex analysis systems.
3Loss of information
If users inspect multiple data samples individually to understand data slices, then slice understanding is improved, but time consumption increases
Solution Approach 1:
The system groups multiple individual data samples into cohesive data slices based on their feature vector similarities. By clustering samples that exhibit similar model behavior patterns, it presents users with consolidated groups rather than requiring inspection of individual samples, maintaining understanding while reducing time consumption.
Solution Approach 2:
The system pre-computes feature vectors for all validation data samples and performs clustering analysis before user inspection. This preliminary processing organizes the data into meaningful slices in advance, so users receive pre-processed, organized groups rather than raw individual samples requiring manual analysis.
4Loss of information
If XAI techniques are applied to all instances to uncover root causes, then model failure understanding is improved, but computational feasibility deteriorates
Solution Approach 1:
Instead of applying XAI techniques to all validation instances, the system applies them selectively to representative samples from each identified data slice. By focusing analysis on key representative examples that capture the essence of each slice's problematic behavior, it achieves comprehensive root cause understanding while maintaining computational feasibility.
Solution Approach 2:
The validation dataset is segmented into distinct data slices based on feature vector clustering. This segmentation allows the system to apply computational resources efficiently by analyzing each slice separately and focusing XAI techniques on representative samples from each segment rather than processing all instances uniformly.
Data Source
AI summary
A workflow for visual analytics using explainable data slices is described, which does not require any metadata. The workflow enables a user to validate and improve a machine learning model that has been trained using a dataset. The workflow generally comprises three phases: an Explainable Data Slice-Finding phase, a Slice Summarization and Annotation phase, and a Slice Error Mitigation phase. In the Explainable Data Slice-Finding phase, the workflow employs pixel attributions to create interpretable features of the dataset. In the Slice Summarization and Annotation phase, the workflow transforms the generated features into visualizations including a ‘Data Slice Mosaic.’ Finally, in the Slice Error Mitigation phase, the workflow leverages the annotation and user-verified spuriousness to mitigate slice errors in the machine learning model.