Explainable Data Value Estimations

• Explainable Data Value Estimations are transparent methods that quantify data importance using techniques such as Shapley values, explainable layers, and statistical inference.
• They integrate model-centric, data-centric, and process-centric approaches to measure feature contributions and support model debugging and auditing.
• By incorporating human-in-the-loop feedback and domain-specific adaptations, these methods enhance fairness, interpretability, and regulatory compliance in ML systems.

Explainable Data Value Estimations aim to provide interpretable, transparent, and actionable assessments of the significance, quality, or impact of data samples, features, or concepts within machine learning systems. This area encompasses model-centric, data-centric, and process-centric approaches, spanning quantitative attribution (e.g., Shapley values), structural model augmentations (e.g., explainable layers), human-in-the-loop specification, and domain-driven formalization. The following sections present key principles, methodologies, and representative empirical findings from recent research, with a focus on approaches that make data value estimations explainable for researchers and practitioners.

1. Foundations and Theoretical Frameworks

Explainable data value estimation builds on foundations in cooperative game theory and information theory. The Shapley value, originally formulated for fair value assignment among players in a coalition, is widely adopted as a rigorous method to attribute value or importance to individual features or data samples within machine learning models. For a value function $V$ and a sample or feature $i$, the Shapley value is given by: $$\Phi(i) = \sum_{S \subseteq F \setminus \{i\}} \gamma(n, |S|) \big(V(S \cup \{i\}) - V(S)\big)$$ where $\gamma(n, m) = \frac{1}{\binom{n}{m}(n+1)}$ weights coalitions by size, and $F$ is the set of all players (features or data points) (Burge et al., 19 Dec 2024).

Conditional Shapley values extend this to account for dependencies among features, especially relevant in tabular data (Olsen, 2023). Information-theoretic approaches—such as conditional entropy—capture user-specific concepts of explainability (Zhang et al., 2020), allowing a regularized trade-off between empirical risk minimization (ERM) and subjective comprehensibility: $$h^{(\lambda)} := \arg\min_{h \in \mathcal{H}} L(h|D) + \lambda \widehat{H}(h|u)$$ where $\widehat{H}(h|u)$ approximates the conditional entropy of predictions given user signal $u$, reflecting user feedback or perception.

2. Architectural and Model-Based Explainability Mechanisms

A prominent direction involves structural network or model modifications to achieve explainable data value estimates.

• Explainable Layers (ExpDNN): ExpDNN introduces a linear "explainable layer" with one-to-one input-feature mapping. The absolute value of each layer weight directly quantifies feature importance after training:

$$\text{FeatureImportance} = [|w_1|, |w_2|, ..., |w_n|]$$

This allows for transparent ranking, feature selection, and model auditing—accounting even for interacting or non-linear inputs (Chen, 2020).

• Statistical Inference for Instancewise Explanations (LEX): Interpretability is cast as an inference problem over latent selection masks, yielding probabilistic, instance-specific feature attributions; the model is regularized for sparsity and fitted via maximum likelihood, where the selector's distribution is task-adaptable (Senetaire et al., 2022).
• Symbolic and Concept-Based Attribution: Neural activation patterns can be mapped to formal concept hierarchies (e.g., Wikipedia-derived ontologies) via OWL-reasoning-based concept induction, enabling neuron-level explanations in human terms and supporting auditability in regulated domains (Dalal et al., 21 Apr 2024).

3. Algorithmic Approaches: Attribution, Uncertainty, and Fairness

Several algorithmic families drive explainable data value estimation:

• Data/Feature Shapley Approaches: Methods such as Data Shapley, Data Banzhaf, and LAVA exploit the marginal contribution principle to rank or filter samples or features. Scalability is addressed via model-based learning (e.g., MLP or sparse regression tree predictors; (Wu et al., 3 Jun 2024)), Harsanyi interaction decompositions, or quantum acceleration for exponential set enumeration (Burge et al., 19 Dec 2024).
• Uncertainty Quantification: Bayesian neural networks and distributional heatmaps expose the confidence of relevance attributions, visualized as percentile-banded maps; this aids trust calibration, dataset curation, and prioritization of new data acquisition (Bykov et al., 2020).
• Influence of Data on Explanations: Beyond impact on accuracy, methods have emerged to quantify the influence of individual training samples on specific explanations (e.g., recourse cost disparities between protected groups), enabling targeted auditing for explanation fairness and reliability (Artelt et al., 5 Jun 2024).

4. Domain-Specific and Task-Adapted Methods

Domain adaptation and user alignment are critical:

• User- and Context-Specific Explainability (EERM): By incorporating user-side signals (e.g., surveys, biosignals) as conditioning variables, models are regularized to produce outputs aligned with subjective explainability; this enables personalized explanations for risk, finance, or social media applications (Zhang et al., 2020).
• Metric-Adaptive Data Valuation (DVR): To accommodate diverse goals (ranking accuracy, fairness, diversity), a metric adapter based on reinforcement learning guides sample value assignment using the metric of interest as a surrogate reward, ensuring both transparency and adaptability across differentiable and non-differentiable objectives (Jia et al., 12 Feb 2025).
• Synthetic Data Evaluation: XAI techniques, such as permutation feature importance, PDP/ICE, Shapley values, and counterfactuals, can diagnose weaknesses in generative models by revealing which features or value ranges are misrepresented—an approach not possible with aggregate statistical or performance metrics alone (Kapar et al., 29 Apr 2025).

5. Practical Workflows and Human-in-the-Loop Systems

Explainable data value estimation increasingly incorporates explicit human oversight and interaction:

• Hybrid Machine-Human, Visual-AI Systems: Frameworks such as DASS combine constrained rule mining, visual analytics, and parameter steering, enabling experts to iteratively refine, validate, and question model outputs in the context of spatial cohort data (e.g., clinical stratification with dose distributions) (Wentzel et al., 2023).
• Sequential and Temporal Data: Bridging numerical-driven and event-based representations, pipelines like EMeriTAte+DF discretize and enrich temporal patterns with interpretable numerical payloads, supporting specification mining over concurrent constituents—beneficial for accurate, explainable time series classification (Bergami et al., 29 May 2025).
• Value Identification from Text (EAVIT): In the context of human value extraction, efficient explainability is achieved by explanation-based fine-tuning, candidate value sampling, and prompt minimization—optimizing both interpretability and computational efficiency in LLMs (Zhu et al., 19 May 2025).

6. Evaluation, Standardization, and Limitations

Reliable, explainable data value estimation depends on evaluation, standardization, and understanding of limitations:

• Ground Truth Benchmarks and Metrics: Evaluation strategies integrate benchmarks with known "true" attributions, local/global performance measures, and agreement metrics such as cosine similarity with ground-truth hyperplanes (Vlassopoulos et al., 2020).
• Verifiable Metrics and Standardization: In data markets (e.g., DeFi), explainability is operationalized via standardized protocols for on-chain, auditable metrics (vTVL) and explicit documentation of data sources, contract methods, and computational logic (Saggese et al., 20 May 2025).
• Caveats and Reliability: Studies highlight that explanation precision (e.g., Shapley value estimates) deteriorates for observations outside the core data distribution, necessitating caution for outlier or rare-case explanations (Olsen, 2023).

7. Impact and Future Directions

Explainable data value estimation has wide-reaching impact for:

• Model selection, debugging, and maintenance: Feature and data value explanations aid practitioners in refining data collection, selection, and feature engineering, enhancing both model performance and trust.
• Fairness, accountability, and auditing: Faithful attributions expose problematic data sources or features contributing to unfair or unreliable model behavior.
• Data pricing and regulation: Interpretable valuation models support transparent, auditable data pricing and regulatory compliance in data-driven markets.

Future research may focus on scalable transfer of learned valuation models across domains, integration with privacy or federated learning, development of hybrid models (combining symbolic and statistical approaches), and human-in-the-loop augmentation for continuous refinement of value attribution.

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Approach/Framework	Explanation Mechanism	Domain/Application
Shapley-based (feature/data)	Marginal value attribution (game theory)	Tabular, sequential, and image data
ExpDNN	Explainable layer weights	General DNNs
LEX/Statistical inference	Probabilistic selection masks	Instancewise explanation (all domains)
Specification mining	Declarative logical clauses (poly-DECLARE)	Multivariate time series
User feedback (EERM)	Conditional entropy, user signal regularization	Personalized explainability
Reinforcement-based (DVR)	RL metric adaptation, Harsanyi decomposition	Recommendation, general ML
Concept induction + ontology	Logical reasoning over knowledge base	Neural activation, semantic explainers

Explainable data value estimations thus provide a comprehensive, mathematically principled, and practical foundation for building transparent, trustworthy, and actionable machine learning systems, spanning the spectrum from rigorous mathematical attribution to pragmatic domain deployment.