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Classification with Conceptual Safeguards
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Proposes conceptual safeguards, a verification layer over independent concept bottleneck models that propagates concept uncertainty to the label and flags concepts for human confirmation to raise coverage at a target selective accuracy.
Problem
Selective classification for concept-based models: abstaining on uncertain predictions to meet a target accuracy while preserving coverage, when concept predictions are uncertain and human experts can confirm concepts.
Approach
Treat a calibrated concept bottleneck (concept detectors plus logistic-regression front-end) as a selective classifier; propagate concept probabilities to label probabilities by enumerating hard concept vectors, and select concepts for human confirmation by a variance-based gain measure under a cost budget.
Key results
- On flycatcher at tau=0.05 with 20% confirmation budget, CS + ImpactConf reaches 84.6% coverage versus 46.2% for Baseline + RandomConf.
- Uncertainty propagation alone raises flycatcher coverage at tau=0.05 from 46.2% (Baseline + RandomConf) to 63.5% (CS + RandomConf) at 20% budget.
- On skincancer at tau=0.2 with 20% budget, CS + ImpactConf attains 85.4% coverage versus 36.6% for CS without confirmation and 0% for baseline at low thresholds.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Propagate concept-prediction uncertainty to the front-end model (ablation) | Conceptual safeguard · Flycatcher (20% confirmation budget, τ=0.05) | coverage: 46.2% to 63.5% | Helped |
| Use targeted ImpactConf rather than random confirmation (ablation) | Conceptual safeguard · Flycatcher (20% confirmation budget, τ=0.05) | coverage: 63.5% to 84.6% | Helped |
| Add random confirmation at a 20% budget (ablation) | Baseline concept bottleneck · Flycatcher (τ=0.05) | coverage: 26.9% to 46.2% | Helped |
| Add uncertainty propagation (ablation) | Conceptual safeguard · SkinCancer | accuracy-coverage trade-off | Helped |
| Add concept confirmation (ablation) | Concept-based models · All datasets | performance | Helped |
Surprising findings
- Gains from confirmation depend strongly on dataset; for warbler and noisyconcepts25 gains are smaller because concept detectors already perform well.
Limitations
- Concept-based models may lose accuracy relative to end-to-end models when concepts do not capture all label information.
- Abstention may exacerbate coverage or performance disparities across subpopulations.
- Assumes human-verifiable concepts and calibrated concept detectors and front-end.
Experimental setup
Datasets: Derm7pt CUB-200-2011 noisyconcepts25 noisyconcepts75
Benchmarks: Derm7pt melanoma Derm7pt skincancer CUB warbler CUB flycatcher noisyconcepts25 noisyconcepts75 CUB-types (multiclass)
Metrics: coverage selective accuracy accuracy-coverage curve
Baselines: X->Y MLP Baseline (independent concept bottleneck) Baseline + RandomConf
Builds on: concept bottleneck models Platt scaling selective classification
Closest papers by meaning
- Hierarchical Selective Classification (NeurIPS 2024 · similarity 0.84)
- FaCT: Faithful Concept Traces for Explaining Neural Network Decisions (NeurIPS 2025 · similarity 0.84)
- An Analysis of Concept Bottleneck Models: Measuring, Understanding, and Mitigating the Impact of Noisy Annotations (NeurIPS 2025 · similarity 0.83)
- Safety Pretraining: Toward the Next Generation of Safe AI (NeurIPS 2025 · similarity 0.83)
- A theoretical design of concept sets: improving the predictability of concept bottleneck models (NeurIPS 2024 · similarity 0.83)
- Testing Semantic Importance via Betting (NeurIPS 2024 · similarity 0.83)
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