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Detection Methods Overview

ShortKit-ML provides multiple complementary methods for detecting shortcuts in embedding spaces. Each method has different strengths and is sensitive to different types of biases.

Why Multiple Methods?

No single method can detect all types of shortcuts. Using multiple methods provides:

  • Comprehensive coverage - Different methods catch different bias patterns
  • Validation - Agreement across methods increases confidence
  • Insights - Each method reveals different aspects of the bias

Methods Comparison

Method What It Detects Strengths Limitations
HBAC Clustering by protected attributes Fast, interpretable dendrograms Assumes hierarchical structure
Probe Information leakage Direct measurement of predictability Requires train/test split
Statistical Dimension-wise differences Identifies specific features May miss non-linear patterns
Geometric Bias directions in subspace Works without labels Requires sufficient samples per group
Equalized Odds TPR/FPR disparities across groups Interpretable fairness metric Requires binary labels
Demographic Parity Positive rate gaps across groups Interpretable fairness metric Requires binary labels
Intersectional Fairness across demographic intersections Catches compounded disparities (Buolamwini & Gebru 2018) Requires 2+ attributes in extra_labels
GroupDRO Worst-group performance gaps Finds hard groups Requires group + task labels
Early-Epoch Clustering Early-epoch shortcut clusters No group labels required Needs early representations
SIS Minimal sufficient input subsets Model-agnostic, concise rationales O(samples × dims) predictor calls
GradCAM Attention on shortcut features Visual interpretability Requires model access, GPU
SpRAy Heatmap clustering (Clever Hans) Detects systematic attention artifacts Requires heatmaps, clustering stability
CAV Concept-level shortcut sensitivity (TCAV) Interpretable concept tests Requires concept/random activation sets
Causal Effect Spurious attributes via causal effect estimation Identifies attributes with near-zero causal effect Requires attributes; optional interventional data
VAE Disentangled latent shortcut dimensions Discovers latent factors; human-interpretable traversals Requires raw images; VAE training cost
Frequency Concentrated class-separable dimensions Embedding-only, no model access needed Assumes linear probe sufficiency
Generative CVAE Spurious attribute influence via counterfactuals Generates embedding-space counterfactuals; measures causal effect Requires binary group labels; seed-sensitive
Shortcut Masking (M01) Mitigation: mask/inpaint shortcut regions Data augmentation for retraining (Teso & Kersting 2019) Requires shortcut regions/dims from detection
Background Randomization (M02) Mitigation: swap foregrounds with random backgrounds Reduces background shortcuts (Kwon et al. 2023) Requires foreground masks; needs at least 2 images
Adversarial Debiasing (M04) Mitigation: remove demographic encoding Adversarial training (Zhang et al. 2018) Requires protected labels; PyTorch
Explanation Regularization (M05) Mitigation: penalize gradients on shortcut regions RRR (Ross et al. 2017) Requires model, images, labels, shortcut masks; PyTorch
Last Layer Retraining (M06) Mitigation: retrain last layer on balanced subset DFR (Kirichenko et al. 2023) Requires task + group labels; sklearn
Contrastive Debiasing (M07) Mitigation: contrastive learning to separate shortcuts Correct-n-Contrast (Zhang et al. 2022) Requires task + group labels; PyTorch

Decision Matrix

Choose methods based on your use case:

graph TD
    A[Start] --> B{Have model access?}
    B -->|Yes| C{GPU available?}
    B -->|No| D[Use: Probe, Statistical, Geometric]
    C -->|Yes| E[Use: All methods including GradCAM]
    C -->|No| F[Use: HBAC, Probe, Statistical, Geometric]

Quick Recommendations

For Production Audits

Use all methods for comprehensive coverage:

detector = ShortcutDetector(
    methods=['hbac', 'probe', 'statistical', 'geometric', 'equalized_odds', 'demographic_parity', 'intersectional', 'groupdro']
)

For Quick Screening

Use fast methods for initial assessment:

detector = ShortcutDetector(
    methods=['statistical', 'probe']
)

For Deep Analysis

Use all methods plus GradCAM for visual insights:

detector = ShortcutDetector(
    methods=['hbac', 'probe', 'statistical', 'geometric']
)
# Plus GradCAM separately for visual analysis
gradcam = GradCAMHeatmapGenerator(model, target_layer)

Interpretation Framework

Risk Assessment

Each method produces risk indicators:

Risk Level HBAC Purity Probe Accuracy Statistical % Geometric Effect Equalized Odds Gap GroupDRO Gap
Low < 60% < 60% < 10% < 0.3 < 10% < 5%
Medium 60-80% 60-80% 10-30% 0.3-0.7 10-20% 5-10%
High > 80% > 80% > 30% > 0.7 > 20% > 10%

Aggregated Risk

The unified ShortcutDetector provides an aggregated assessment via the summary() method. The aggregation strategy is controlled by the condition_name parameter:

Condition Strategy Best For
indicator_count (default) Counts total risk indicators. 2+ = HIGH, 1 = MODERATE, 0 = LOW Simple audits, general use
majority_vote Counts methods with indicators as votes Conservative consensus-based screening
weighted_risk Weights each detector by evidence strength (probe accuracy, stat significance, HBAC confidence, geometric effect size) Paper results, nuanced analysis
multi_attribute Cross-references risk across sensitive attributes; escalates when multiple attributes flag risk Multi-demographic analysis (race + sex + age)
meta_classifier Trained sklearn meta-classifier on detector features, or heuristic fallback Advanced pipelines with synthetic training data 
# Default aggregation
detector = ShortcutDetector(methods=['hbac', 'probe', 'statistical'])
detector.fit(embeddings, labels)
print(detector.summary())

# Weighted risk for nuanced scoring
detector = ShortcutDetector(
    methods=['hbac', 'probe', 'statistical', 'geometric'],
    condition_name='weighted_risk',
)
detector.fit(embeddings, labels)
print(detector.summary())

# Access individual method results
results = detector.get_results()
print(results['probe']['accuracy'])  # Probe accuracy
print(results['hbac']['shortcut_detected'])  # HBAC detection result

Detailed Method Guides

Learn more about each method:

Method Limitations

All methods have limitations you should be aware of:

  1. Sample Size - Most methods need sufficient samples per group (n > 30)
  2. Linearity - Some methods assume linear relationships
  3. Independence - Assumes embeddings are i.i.d. samples
  4. Single Protected Attribute - Most fairness methods assume one protected attribute; use Intersectional Analysis for multiple attributes.