Concept-Guided Explanations in ML

• Concept-guided explanations are frameworks that link model predictions to high-level, semantically coherent concepts (e.g., stripes or wings) across various domains.
• They employ techniques such as TCAV and CAR, alongside automated and human-in-the-loop methods, to quantify and refine concept importance.
• Applications span vision, text, tabular, and graph data, improving model debugging, fairness assessment, and optimization through actionable insights.

Concept-guided explanations refer to machine learning interpretability frameworks that attribute model predictions to human-understandable, high-level concepts, as opposed to raw features or pixel-level cues. These methods aim to reconcile deep neural models’ internal reasoning with human abstraction, providing explanatory units such as “stripes,” “wheel,” or “has wings.” This paradigm has become foundational in post-hoc and ante-hoc explainable AI, spanning classification tasks in vision, language, tabular, and graph domains. The evolution from concept activation vectors to advanced region-based, generative, and uncertainty-aware frameworks has enabled more comprehensive, robust, and actionable explanations, often integrating causal assessment and human-in-the-loop workflows.

1. Formalization of Concepts and Explanation Units

Concepts in concept-guided explanation systems are sets of semantically related patterns or attributes that appear throughout data and model activations. Concept definitions may arise:

• Through manual annotation (e.g., “striped” images for zebra detection) (Ghorbani et al., 2019).
• Via unsupervised segment clustering and activation analysis (ACE) (Ghorbani et al., 2019); (Alam et al., 2022).
• By leveraging pre-trained models for concept extraction (CLIP, SAM, GPT-4o) (Liu et al., 2024); (Sun et al., 2023).

Formally, a concept $c$ is described by a set of positive examples $P^c = \{x^{c,1}, ..., x^{c,N}\}$ and negatives $N^c = \{x^{¬c,1}, ..., x^{¬c,N}\}$, mapped into latent activations $z_i^+ = g(x^{c,i})$, $z_i^- = g(x^{¬c,i})$ (Crabbé et al., 2022). Concepts may also be realized as Boolean predicates over tabular rows (Pendyala et al., 2022), linguistic clusterings (Alam et al., 2022), or localized graph motifs (Magister et al., 2021).

2. Mechanisms for Quantifying Concept Importance

The canonical quantification involves probes that assess how the presence of a concept influences model predictions. Key mechanisms include:

• Concept Activation Vectors (CAV): Linear classifiers separate concept vs. non-concept activations. The TCAV score for class $k$, concept $c$, layer $\ell$ is

$$\mathrm{TCAV}_{c,k,\ell} = \frac{1}{|\mathcal{X}_k|} \sum_{x \in \mathcal{X}_k} \mathbf{1}\left( \frac{\partial f_k(x)}{\partial f^{[\ell]}(x)} \cdot v_c > 0 \right),$$

interpreting the fraction of class-$k$ inputs whose decisions are sensitive to concept $c$ (Yeh et al., 2022).

• Concept Activation Regions (CAR): Kernelized support vector classifiers define nonlinear concept regions, generalizing CAVs and enabling invariance to latent-space isometries. The region $H^c = \{z : f_c(z) = +1\}$ supports both global scoring and local feature attributions via concept density (Crabbé et al., 2022).
• Sufficiency and Necessity Scores: Sufficiency tests $E[f(x) \mid c(x) \geq \theta]$ (does concept $c$ suffice for prediction?), while necessity examines $E[c(x) \mid f(x) = +1]$ (is concept $c$ needed for prediction?) (Feng et al., 2024).
• Completeness: Measures whether the available concepts fully explain model predictions, often implemented via decoding accuracy from concept scores to labels (Ghorbani et al., 2019); (Yeh et al., 2022).

3. Automated and Human-in-the-Loop Concept Discovery

Scaling concept discovery involves multiple unsupervised and interactive methods:

• ACE (Automated Concept-based Explanation): Segments inputs (via SLIC, SAM), embeds segments in latent space, clusters to discover candidate concepts, filters for coherency/coverage, and quantifies importance via TCAV (Ghorbani et al., 2019); (Sun et al., 2023).
• Vision-LLMs: Zero-shot extraction driven by CLIP, BLIP, or GPT-based queries, returning concise concept lists and activation predicates (Liu et al., 2024).
• Preference learning and generative models: RLPO incorporates reinforcement learning and generative diffusion models to synthesize new concept exemplars guided by TCAV feedback, enhancing discovery of otherwise missed or abstract concepts (Taparia et al., 2024).
• Human-in-the-loop annotation: Interactive platforms (e.g., ConceptExplainer) allow browsing, labeling, merging, and bias tagging of clusters, leveraging auto-annotations from ontologies and sensitive lexica (Alam et al., 2022); (Huang et al., 2022).

4. Practical Realizations Across Data Modalities

Concept-guided frameworks are instantiated in diverse domains:

• Vision: Segment-based explainers map objects, textures, or regions to concepts and render explanations as spatial heatmaps (e.g., SEG-MIL-CBM overlays concept maps on image regions) (Eisenberg et al., 5 Oct 2025); (Sun et al., 2023).
• Text: Object-centric architectures with slot attention discover textual aspects or topics; LLM-evaluation guides concept refinement for comprehensibility (ECO-Concept) (Sun et al., 26 May 2025). Model-agnostic ConLUX deploys concept-aware local explainers, replacing word-level predicates with high-level topics for LIME, SHAP, Anchor, and LORE (Liu et al., 2024).
• Tabular: Boolean predicates over columns specify concepts; synthetic or generative sampling ensures adequate coverage for TCAV-based attribution and fairness assessment (Pendyala et al., 2022).
• Graph: GCExplainer clusters node/graph activations into motifs or subgraph patterns, supporting global completeness evaluation and motif labeling (Magister et al., 2021).
• Sequential/Agent Systems: State2Explanation establishes embedding alignment between state-action pairs and concept annotations for dual-purpose improvement of RL learning and user-facing explanations (Das et al., 2023).

5. Robustness, Uncertainty, and Faithfulness

Several recent trends address the reliability and fidelity of concept explanations:

• Uncertainty-aware estimation (U-ACE): Bayesian inference on probe weights mitigates overfitting to spurious or under-sampled concepts; robust to missing, overcomplete, or noisy concept banks (Piratla et al., 2023).
• Counterfactual and causal frameworks: Concept-guided counterfactual generation (CoLa-DCE) restricts diffusion-based perturbations to semantic concept channels, enforcing minimality and transparency of “what changed where” (Motzkus et al., 2024). Causal concept explanations compute probability-of-sufficiency for concept interventions, operationalizing “if-then” queries on model outcomes while requiring explicit causal structure and invertible concept mapping (Bjøru et al., 2 Dec 2025).
• Faithfulness metrics: Deletion/insertion AUCs, compactness (SSC/SDC), and surrogate fidelity are now routinely reported to demonstrate that concept scores truly reflect the underlying model logic (Aghaeipoor et al., 2023); (Sun et al., 2023); (Liu et al., 2024).

Method	Faithfulness (AUC)	Completeness	Uncertainty Modelling	Modality
ACE/TCAV	Good	Yes	No	Vision, Tabular, Graph
CAR	Excellent	Yes	No	Vision
U-ACE	Good	Yes	Yes	Vision, Scene
SEG-MIL-CBM	Excellent	Yes	No	Vision
CoLa-DCE	Excellent	Yes (CF)	Yes (CF)	Vision
ECO-Concept	Good	Yes	Yes (LLM)	Text
ConLUX	Good	Yes	Partial	Text, Vision
RLPO	Good	Yes	Preference-weighted	Vision

6. Applications, Evaluation, and Best Practices

Concept-guided explanations have effected considerable impact:

• Debugging and bias detection: Revealing spurious correlations (background, labeling inconsistencies), identifying dataset bias, and surfacing hidden shortcut features (Eisenberg et al., 5 Oct 2025); (Huang et al., 2022).
• User studies: Demonstrated increased human trust and user task performance, both for expert and non-expert audiences—ConceptExplainer’s navigable UI validates interpretability at instance, class, and global levels (Huang et al., 2022).
• Model selection and optimization: Revealing model preference for certain concepts guides architecture or optimizer choice (Feng et al., 2024).
• Fairness assessment: TCAV-fairness metrics offer layer-wise diagnosis of protected attribute usage, correlating with demographic parity gaps (Pendyala et al., 2022).
• Reinforcement learning acceleration: State2Explanation integrates concept shaping into reward functions, speeding convergence and enhancing human understanding (Das et al., 2023).

Recommended practices include:

• Layer selection balancing abstraction and coherence (Yeh et al., 2022).
• Use of random baseline vectors for statistical validation (Yeh et al., 2022).
• Checking completeness to avoid missing critical concepts (Ghorbani et al., 2019).
• Systematic human-in-the-loop evaluation for annotation and iterative refinement (Alam et al., 2022).
• Explicit modelling of uncertainty, especially in large or noisy concept sets (Piratla et al., 2023).

7. Limitations, Open Problems, and Future Directions

Current limitations include:

• Dependency on quality and diversity of concept examples; inadequate concept banks can fail to explain key model behaviors (Yeh et al., 2022).
• Potential for spurious or non-causal concept associations, mitigated with causal effect estimation or robust generative sampling (Piratla et al., 2023); (Motzkus et al., 2024); (Bjøru et al., 2 Dec 2025).
• Scalability challenges as concept sets grow, particularly for interactive or generative frameworks (Taparia et al., 2024).
• Trade-offs between surrogate fidelity and interpretability, notably in joint distillation models (Sousa et al., 2022).
• Need for explicit causal structure and reconstructive mappings in counterfactual/SCM-based explanations (Bjøru et al., 2 Dec 2025).

Active research addresses:

• Automated, scalable concept discovery (preference learning, active querying) (Taparia et al., 2024); (Sun et al., 26 May 2025).
• Unified model-, task-, and modality–agnostic frameworks (ConLUX, EAC), supporting vision, text, tabular, and graph data (Liu et al., 2024); (Sun et al., 2023).
• Further integration of uncertainty quantification to robustify explanations and support interactive debugging (Piratla et al., 2023).
• Expanding causal concept assessment (probability-of-sufficiency, causal modeling) to support actionable counterfactuals and fair explanations (Bjøru et al., 2 Dec 2025).

In summary, concept-guided explanations now constitute a mature, multi-modal field of interpretable machine learning, with ongoing progress toward robust, scalable, and causally-grounded explanation protocols.