AI BIAS ANALYSIS
Unveiling Hidden Biases in AI Datasets with ConceptScope
A novel framework for scalable, automated, and interpretable dataset characterization using Sparse Autoencoders.
Quantifiable Impact of ConceptScope
ConceptScope delivers significant advancements in bias detection and model robustness.
0.76
Average AUPRC for Concept Prediction
100%
Accuracy in Known Bias Detection (Waterbirds)
2.45
Average Bias Concepts Discovered per Class
Deep Analysis & Enterprise Applications
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Target concepts are the essential features for recognizing a class, like the body or shell of a sea turtle. Their absence significantly hinders correct classification.
Context concepts frequently co-occur with a class but are not necessary for recognition, such as a beach background for a sea turtle image.
Bias concepts are context concepts that disproportionately co-occur with a class in the dataset, like sandy environments appearing far more frequently than ocean or tropical scenes for sea turtle images.
72%
Concept Prediction F1 Score
ConceptScope significantly outperforms VLM-based baselines in accurately predicting the presence of visual concepts across diverse datasets, achieving an F1 score of 72% (Table 1).
ConceptScope Framework Overview
Our framework operates in two distinct stages to analyze visual datasets.
Construct Concept Dictionary (SAEs on Vision Models)
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Quantify Concept Activations (Patch & Image Levels)
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Interpret Learned Concepts (Top-Activating Images & LLMs)
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Categorize Concepts (Target, Context, Bias based on Alignment & Strength)
| Feature | ConceptScope (Ours) | Baseline Methods |
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| Bias Discovery Mechanism |
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| Applicability |
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Diagnosing Model Robustness with ConceptScope
ConceptScope provides a novel diagnostic tool for model robustness by partitioning test data into subgroups based on target and bias concept strengths. This allows for evaluation of model generalization under varying distribution shifts, revealing that models performing well on typical (high target-high bias) samples also tend to perform better on rare or outlier (low target-low bias) settings.
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Estimated Annual Savings
$0
Hours Reclaimed Annually
0
Your AI Implementation Roadmap
A phased approach to integrating ConceptScope into your data analysis workflow.
Discovery & Concept Definition
Collaborate to define key concepts relevant to your domain and data. Train SAEs on your visual data to create a custom concept dictionary.
Bias Identification & Characterization
Apply ConceptScope to your datasets to automatically identify target, context, and bias concepts, quantifying their distributions.
Model Diagnostics & Robustness Evaluation
Utilize concept-based subgrouping to evaluate model robustness and identify areas for improvement under distribution shifts.
Strategic Integration & Optimization
Integrate ConceptScope insights into your data curation pipelines and model development cycles for continuous improvement.
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