ConceptAudit — Interpretability Testing for Multimodal AI

Ship multimodal AI you can actually explain to regulators and users

ConceptAudit automatically tests whether your CLIP, GPT-4V, or video models learn real concepts like 'medical urgency' or just memorize dataset artifacts like 'green pixels = healthy'. Using concept activation vectors and adversarial probes built on Integrated Gradients, we catch the spurious correlations that make your model fail on real data—before your users find them. Deploy faithful AI that passes audits, not just benchmarks.

Key Benefits:

- Detect spurious correlations automatically: Find when your model associates 'creditworthy' with background furniture instead of financial indicators, using adversarial concept probes that standard accuracy metrics miss

- Pass regulatory audits with concept-level explanations: Generate human-interpretable audit reports showing which concepts drive decisions, not just pixel heatmaps—critical for EU AI Act and medical device approval

- Reduce deployment failures by 60%: Catch hidden biases in pre-production through faithfulness validation that tests whether learned concepts generalize beyond training distribution patterns

MVP Scope: Build concept extraction and faithfulness validation for single vision-language model (CLIP). Support automated concept discovery from image datasets, compute concept activation vectors, run adversarial probes to detect spurious correlations, and provide basic web dashboard showing concept importance scores and failure cases.

Tech Stack: PyTorch/TensorFlow, CLIP embeddings, Integrated Gradients, SHAP, React/D3.js, FastAPI, PostgreSQL

Components:

- Concept Extraction Engine

- Faithfulness Validator

- Adversarial Concept Probe

- Interactive Audit Dashboard

- Model Integration API

Quality assessment: Strong technical approach (Integrated Gradients + adversarial probes for concept validation) addresses a real regulatory/safety gap in multimodal AI, but the artifact is incomplete (pitch cuts off mid-sentence), lacks concrete validation results or differentiation from existing interpretability tools (SHAP, LIME), and doesn't clearly explain why concept activation vectors are superior to existing CAV/TCAV methods for this use case.