Explainable Detection Paradigms

• Explainable Detection Paradigms are frameworks that rigorously separate algorithmic output from human cognitive interpretation to provide actionable explanations.
• They employ techniques such as gradient-based attributions, multi-class heatmaps, and symbolic reasoning to quantify feature contributions in detection tasks.
• These paradigms are evaluated using quantitative metrics and tailored user insights, ensuring robust, transparent, and operationally effective detection systems.

Explainable Detection Paradigms are algorithmic and cognitive frameworks designed to furnish not only predictions but also interpretable explanations in detection problems—from unsupervised anomaly detection and multiclass object detection to symbolic fraud analysis. These paradigms systematically separate the algorithmic production of explanations from human cognitive interpretation, unify model-agnostic and model-specific explanation protocols, and establish quantitative and qualitative metrics to evaluate explanation fidelity and utility. The evolution of explainable detection has yielded methods that range from gradient-based attributions to symbolic reasoning, multi-channel heatmaps, perturbation-based saliency, and competitive learning architectures.

1. Formalization: Separation of Explanation and Interpretation

The foundational distinction in state-of-the-art explainable detection is a rigorous separation between explainability and interpretability. Explainability is viewed as an algorithmic process that, given an input $x$ and a detection function $F$, computes a score $F(x)$ and an explanation $B(x)$ where each component quantifies the feature-wise contribution ("blame") to the anomaly or detection event. Interpretability resides in the cognitive process, wherein an expert maps the machine-generated $B(x)$ onto discrete diagnoses or action sets by contextualizing it with domain knowledge $C$.

This separation is mathematically grounded as follows (Sipple et al., 2022):

• Explainability (algorithmic): Compute $B(x) \in [0,1]^D$, with $b_d$ quantifying the contribution of input dimension $d$ to the detection decision.
• Interpretability (cognitive): Expert maps $B(x)$ to diagnosis $I(B(x)) \in \mathcal{P}(C)$.

Information-theoretically, the detection system is a transmitter emitting $(F(x), B(x))$ tokens; the human is the receiver, interpreting the message in context.

2. Model Families and Explanation Mechanisms

Detection paradigms span a variety of model families, each with accompanying explanation techniques.

2.1 Gradient-based Attribution in Anomaly Detection

Unsupervised detectors $F: \mathbb{R}^D \to [0,1]$ employ Integrated Gradients (IG) with systematically chosen nearest-exemplar baselines. For input $x$ and baseline $x'$, attributions are computed along a path $P(\alpha) = x + \alpha(x' - x)$: $$IG_u(x, x') = \int_0^1 (x'_u - x_u) \cdot \frac{\partial F(P(\alpha))}{\partial P_u(\alpha)} d\alpha$$ This yields contrastive, proportional, and sensitivity-respecting blame scores per feature, supporting completeness: $\sum_u IG_u(x, x') = F(x') - F(x)$ This approach is shown to produce significantly lower attribution error compared to SHAP and LIME across real-world datasets (6.0% IG vs 12.8% LIME on Aircraft; 7.5% IG vs 13.8% LIME on VAV) (Sipple et al., 2022).

2.2 Multi-Type, Multi-Class Heatmap Generation

The MultiTypeFCDD model (George et al., 14 Nov 2025) extends fully-convolutional detection to produce multi-channel anomaly heatmaps, enabling simultaneous detection and differentiation of multiple anomaly types. Each type $k$ is assigned a heatmap $A'_k(X)$ via: $A_k(X) = \sqrt{\phi_k(X; W)^2 + 1} - 1$ and upsampled to dense pixel-wise outputs. An image-level classification score for type $k$ is: $z_{ik} = \frac{1}{uv} \|A_k(X_i)\|_1$ The loss penalizes false positives on normal images and rewards activation on anomalies: $$L_{total}(W) = \frac{1}{MN} \sum_{i=1}^N \sum_{k=1}^M \left[ (1-y_{ik}) z_{ik} - y_{ik} \log(1 - \exp(-z_{ik})) \right]$$ This delivers competitive AUROC (94–96%) and type-wise localization with a lightweight architecture (5.5 M parameters) suitable for embedded systems.

2.3 Symbolic Function Search

Deep Symbolic Classification (DSC) reframes detection as a discrete search over symbolic analytic functions $f: \mathbb{R}^n \to \mathbb{R}$, with functions composed from variables $V$, constants $C$, and operators $O$. DSC employs reinforcement learning with policy gradients to optimize directly for imbalance-robust metrics (e.g., F$_1$) or custom objectives. The model selection explicitly trades off predictive power and expression complexity: $L(f) = M(f) - \lambda E(f)$ where $E(f)$ quantifies the symbolic complexity (token cost), and $M(f)$ is the metric (e.g., F$_1$). Final classifiers such as,

$\text{"Classify as fraud iff (type=transfer) \land (externalDest=True) \land (amount-maxDest_7 > -0.15)"}$

are directly auditable and yield competitive accuracy (F$_1$ 0.78 vs XGBoost 0.82) (Visbeek et al., 2023).

3. Perturbation-Based and Hierarchical Masking Approaches

Model-agnostic black-box explainability is achieved through hierarchical perturbation and masking. BODEM (Moradi et al., 2023) uses a multi-level block partitioning and randomized masking to identify salient regions:

• Mask generation: At each level $\ell$, random masking of blocks and their neighbors guided by previous saliency.
• Saliency estimation: Quantifies importance of blocks via IoU drop when masked, recursively refining the saliency map.

BODEM surpasses D-RISE and LIME in metrics: lower Deletion AUC (0.058 vs D-RISE 0.113), higher Insertion AUC (0.875 vs D-RISE 0.612), and better convergence. The approach is purely black-box, requiring only bounding-box outputs.

4. Attribution Metrics and Quantitative Evaluation

Rigorous evaluation of explainable detection relies on algorithmically defined metrics. Attribution Error is used for feature blame alignment (Sipple et al., 2022): $$\epsilon^{(i)}(x) = \frac{1}{|D|} \sum_{d=1}^D | B^{(i)}_d(x) - \beta_d(x) |$$ with ground-truth blame $\beta_d$ determined by expert annotations.

For object detection, ODExAI (Nguyen et al., 27 Apr 2025) introduces core metrics:

Metric Name	Formula	Purpose
Pointing Game (PG)	$$\frac{1}{N} \sum_{n=1}^N \mathbf{1}(\arg\max_{(x,y)} S_n(x,y) \in \text{bbox}_n)$$	Localization Accuracy
Objectness Agreement (OA)	$$\frac{1}{M} \sum_{m=1}^M (AUC_{\text{ins},m} - AUC_{\text{del},m})$$	Causal Faithfulness
Sparsity	$\frac{S_{\max}}{S_{\text{mean}}}$	Concentration of attribution
Runtime	measured in seconds	Computational complexity

Region-based perturbation methods (D-CLOSE) yield higher OA (0.86), whereas CAM-based methods (G-CAME) achieve better PG (0.96) with substantially lower runtime.

5. Cognitive Science and Symbolic Reasoning Integration

Explainable detection paradigms increasingly incorporate cognitive science principles. For example (Sipple et al., 2022):

• Distance metrics (L1/L2) for selecting prototype normals or exemplars reflect psychological similarity models.
• Contrastive explanations ("Why not normal?") parallel human preference for counterfactual and comparative reasoning.

Competitive Learning Intrusion Detection Systems (X-IDS) (Ables et al., 2023) leverage SOM/GSOM/GHSOM architectures to yield intrinsically interpretable cluster-based explanations, visualized via U-Matrices, component planes, and occupancy histograms. These methods allow for statistical and topological navigation, supporting explanation-driven model refinement and trust.

6. Domain-Specific Paradigms and Generalizability

Explainable detection frameworks are highly adaptable across domains:

• Fraud, intrusion, and misinformation detection deploy models such as DSC, SOM/GHSOM, DANN+LIME (domain adaptation + local surrogate explanation), and DISCO (graph + personalized PageRank masking) (Visbeek et al., 2023, Ables et al., 2023, Joshi et al., 2022, Fu et al., 2022).
• Each paradigm incorporates explanation output tailored to operational stakeholders—feature importance charts, rule sets, prototype matching, contrastive interventions, or semantic rationale traces.

Model-agnostic explainers such as SHAP, LIME, ProtoDash, and Boolean Column Generation complement deep learning approaches, providing global, local, contrastive, and prototype-guided insights for regulatory, operational, and end-user consumption, with explicit trade-offs in accuracy versus transparency.

7. Challenges, Evaluative Trade-Offs, and Future Directions

Key challenges persist:

• Balancing computational feasibility, fidelity, and localization, particularly in large-scale image/video or multi-class settings (Nguyen et al., 27 Apr 2025, George et al., 14 Nov 2025).
• Ensuring axiomatic validity (efficiency, symmetry, dummy, linearity) of attributions—addressed by Baseline Shapley-based methods (Kuroki et al., 2023).
• Bridging model-agnostic and model-specific approaches, especially for black-box industrial deployment (D-MFPP, D-Deletion) (Andres et al., 28 Oct 2024).

Future directions include:

• More rigorous integration of cognitive processes into explanation workflows.
• Unified paradigms for multi-modal or multi-type detection across modalities (images, videos, text) (Zhang et al., 1 Jun 2025, Cao et al., 28 Nov 2025).
• Dynamic baseline and prototype selection, human-in-the-loop refinement, and process-based reasoning (e.g., Cognition Chain for psychological stress (Wang et al., 18 Dec 2024)).
• Expansion of explanation metrics to address robustness, multi-instance confusion, and application-centric needs.

Explainable Detection Paradigms collectively advance the field beyond opacity, by anchoring explanation generation in mathematically principled, cognitively intuitive methods evaluated by rigorous metrics. The convergence of algorithmic and cognitive perspectives enables detection systems to provide not just accurate outputs but actionable, auditable, and human-aligned rationales—essential for transparent, trustworthy, and operationally robust AI deployment.