Explainable AI in Scientific Discovery

• Explainable AI in scientific discovery is a framework that emphasizes transparency, interpretability, and contextualization of machine learning outputs with domain-specific scientific principles.
• It integrates domain knowledge directly into model design and feature engineering to produce scientifically credible, reproducible, and actionable insights.
• Techniques such as SHAP, LIME, and physics-informed neural networks enable researchers to rationalize complex decisions and accelerate hypothesis testing.

Explainable AI (XAI) in scientific discovery encompasses a set of methodologies, frameworks, and philosophical stances aimed at ensuring that ML models used for extracting knowledge from observational or simulated scientific data produce outputs that can be rationalized, inspected, and linked to established or novel scientific principles. XAI is distinguished from traditional AI by its emphasis on transparency, interpretability, and the meaningful integration of domain knowledge, ultimately with the goal of producing results that are credible, reproducible, and actionable within scientific workflows.

1. Core Elements: Transparency, Interpretability, and Explainability

A canonical XAI framework for scientific discovery is structured around three pillars:

• Transparency refers to the degree to which the structure, components, and training procedures of an ML model are openly accessible and mathematically tractable. This covers choices in architecture (e.g., the selection of a kernel in Gaussian Processes, or the depth and connectivity of neural networks), regularization, and learning algorithms. Transparent models enable researchers to rationally justify model design decisions and to analyze algorithmic behavior at a structural level (Roscher et al., 2019).
• Interpretability is concerned with rendering the learned representations and decisions of a model comprehensible to domain experts. This often involves constructing interpretable proxy models, such as locally linear approximations (e.g., LIME), sensitivity analyses, relevance scoring, and other surrogates that map complex behaviors to understandable input–output relations (Roscher et al., 2019, Tuckey et al., 2020, Samek et al., 2019).
• Explainability integrates both transparency and interpretability, requiring that outputs not only be understandable but also contextualized using domain-specific scientific knowledge. True explainability requires not just answering “what” or “how” a model predicts, but “why”—in terms of linking algorithmic discoveries back to physical, chemical, biological, or other scientific laws (Roscher et al., 2019, Emmert-Streib et al., 2020).

These principles function synergistically: transparency grounds the model in a tractable space; interpretability ensures that outputs are human-comprehensible; explainability aligns these outputs with domain concepts, thereby granting scientific value.

2. Integration of Domain Knowledge and Model Design

Domain knowledge represents the fundamental bridge between data-driven model outputs and meaningful scientific explanations. Its incorporation occurs at multiple levels:

• Model Design and Regularization: Embedding scientific priors (e.g., conservation laws, symmetry constraints, inductive biases) directly into the hypothesis space or loss function. For instance, employing physics-informed neural networks for fluid dynamics ensures predictions respect invariants such as divergence-free velocity fields. This is also witnessed in genomics (e.g., visible neural networks aligning the model architecture with gene ontology hierarchies) and inverse problems solved by GP priors with known differential operators (Roscher et al., 2019, Behandish et al., 2022, Liu et al., 2020).
• Interpretation and Consistency Checks: Domain knowledge enables the identification and decomposition of learned latent variables (for example, mapping autoencoder representations to physical parameters) and the execution of post hoc consistency tests (e.g., checking whether predicted variables satisfy known equations of state or conservation laws) (Roscher et al., 2019, Emmert-Streib et al., 2020, Liu et al., 2020).
• Feature Engineering and Attribute Selection: Transforming raw data into scientifically meaningful features (e.g., actionable material attributes like crystal size or porosity in materials science) ensures interpretability and facilitates counterfactual reasoning—by enabling direct manipulation of interpretable input dimensions (Liu et al., 2020, Jin et al., 26 Aug 2025).

Explicit domain integration offers two principal benefits: regularizing models in low-data regimes (thus improving scientific consistency), and rendering outputs into forms immediately relevant for experimentation or hypothesis generation.

3. Techniques and Methods for Explainability

A suite of XAI techniques is central to extracting and rationalizing ML model decisions in the sciences:

Technique Category	Example Methods	Typical Output Form
Feature Attribution	Gradients, SHAP, LIME, Integrated Grads	Feature importances, saliency maps
Surrogate Models	LIME, Decision Trees, Rule Lists	Simple interpretable models (linear/proxy)
Prototype/Activation	Activation Maximization, Prototypes	Maximally activating inputs, representative
Counterfactual/Contrastive	Anchors, Counterfactual Search	Minimal changes to flip a decision
Architectural Design	Physics-informed NNs, Visible NNs	Human-aligned intermediate representations
Knowledge Graphs	Structured artifact extraction	Interconnected concepts/evidence

These methods are applied variously to exceed feature or instance-level insight: for example, Layer-wise Relevance Propagation (LRP) and Grad-CAM for pixel/voxel-level visualization in imaging; GNNExplainer and attention-based message passing in graph-based molecular modeling; GANs with tunable “attribute knobs” for materials science; and symbolic regression for translating learned functions to human-interpretable equations (Samek et al., 2019, Jiménez-Luna et al., 2020, Liu et al., 2020, Li et al., 2021, Baulin et al., 23 May 2025).

An illustrative propagation algorithm such as LRP is described by the backward update:

$$R_i^{(l)} = \sum_j \frac{a_i^{(l)} w_{ij}^{(l,l+1)}}{\sum_{i'} a_{i'}^{(l)} w_{i'j}^{(l,l+1)} + \epsilon} R_j^{(l+1)}$$

4. Applications and Impact in Scientific Discovery

Explainable AI has materially impacted a wide range of disciplines:

• Physical Sciences: Predictive and explanatory models for intuitive physics, materials properties (e.g., glass formation, nanoparticle energetics), and solution of inverse problems with scientifically interpretable parameters (Roscher et al., 2019, Li et al., 1 Feb 2024, Liu et al., 2020).
• Life and Medical Sciences: Disease diagnosis (e.g., cancer subtyping, COVID-19 imaging), drug discovery (lead selection, QSAR, toxicity), and genomics (interpretation of gene ontology structure) (Jiménez-Luna et al., 2020, Alizadehsani et al., 2023).
• Earth and Environmental Sciences: Weather prediction, hydrology, hazard assessment—supported by hybrid models that merge data-driven learning with process-based physical models, where XAI is used both for model selection and assurance of physical consistency (Huang et al., 12 Jun 2024, Mengaldo, 15 Jun 2024).
• Autonomous Science Agents: Multi-agent and agentic systems (e.g., Aleks, AI Scientist-v2), wherein orchestration of hypothesis generation, experiment design, analysis, and interpretation is executed in an explainable, memory-driven manner—combining LLMs, domain agents, and visual/ textual critique to autonomously compose interpretable, academically valid scientific outputs (Yamada et al., 10 Apr 2025, Jin et al., 26 Aug 2025).

Scientific impact is twofold: improved trust and adoption of AI models (as their decisions and failure modes become auditable), and accelerated or novel discoveries powered by the capacity to test hypotheses, explore Rashomon sets of models, and iterate interactively between model outputs and domain knowledge (Li et al., 1 Feb 2024, Yamada et al., 10 Apr 2025).

5. Critical Challenges and Controversies

Persistent challenges confront the adoption and trustworthiness of XAI in science:

• Explanation Inconsistency: Divergent interpretations can arise depending on the choice of model, feature set, or attribution method. The concept of a Rashomon set (models of comparable accuracy but differing internal mechanisms) implies that explanations must be evaluated over model ensembles, not just single instances (Li et al., 1 Feb 2024).
• Lack of Formal Correctness: Feature attribution and related XAI methods can systematically misattribute importance to variables unrelated to the scientific target, especially in the presence of suppressor variables or correlated noise. The absence of formally defined explanation correctness—i.e., verifying that explanations highlight only causally/statistically relevant variables—remains a fundamental obstacle (Haufe et al., 22 Sep 2024).
• Partial Interpretability and Opaqueness: Tradeoffs between model interpretability and predictive power are inherent, with high-dimensional or highly non-linear models inevitably presenting opaque formulations resistant to natural language explanation, especially outside of domains with well-defined mathematical theories (Emmert-Streib et al., 2020).
• Evaluation and Standardization: There is a paucity of agreed objective metrics for evaluating explanation quality, faithfulness, and robustness, particularly for domain-specific scientific applications. Benchmark datasets and synthetic controls are necessary for systematic validation (Haufe et al., 22 Sep 2024, Huang et al., 12 Jun 2024).
• Scaling, Efficiency, and Human Factors: As scientific data and model scale rise, computational cost, explanation stability, and the alignment of outputs to stakeholders' needs require new frameworks (e.g., holistic XAI pipelines, tailored explanation agents) (Paterakis et al., 15 Aug 2025, Huang et al., 12 Jun 2024).

6. Methodological Advances and Future Directions

Emerging approaches reflect a trend toward more rigorous, holistic, and agentic systems:

• Automated Reasoning and Explanation Selection: Integration of induction (machine learning) with deduction (automated reasoning/SAT solvers) enables the generation of formally verified, mathematically grounded explanations. Taxonomies of explanation selection now encompass necessity, sufficiency, minimality, anomaly, and contrastivity, informed by social and cognitive science (Iser, 24 Jul 2024).
• Holistic Workflow Integration: Next-generation frameworks, such as Holistic XAI (HXAI), embed explanation into all machine learning workflow stages—delivering data provenance, model training transparency, quality metrics, and user-adapted narratives through LLM-powered agents and question banks (Paterakis et al., 15 Aug 2025).
• Active Inference Architectures: Systems that close the loop between internal model reasoning (with simulation and counterfactuals), persistent knowledge graph evolution, continuous empirical feedback, and human judgment aim to narrow the abstraction, reasoning, and reality gaps that currently limit AI-driven science. Bayesian planners and causal self-supervised foundation models are prominent tools in these systems (Duraisamy, 26 Jun 2025).
• Knowledge Landscape Synthesis: Frameworks like the Discovery Engine formalize scientific knowledge as tensors and graphs distilled from literature, enabling agents to navigate, synthesize, and generate evidence-backed new hypotheses with full provenance and explainability (Baulin et al., 23 May 2025).
• Agentic Scientific Automation: Multi-agent systems with built-in domain knowledge, persistent memory, and capacity for explanatory interaction (e.g., Aleks) have demonstrated autonomous scientific discovery with domain-relevant and interpretable outputs, particularly when compared through ablation studies to purely data-driven methods (Jin et al., 26 Aug 2025).

Future developments are anticipated in formalizing explanation correctness, aligning XAI with user/stakeholder needs, incorporating causal reasoning, and fostering automated closed-loop cycles from hypothesis to experimental validation. The broader aim remains a principled, verifiable, and collaborative AI-scientist partnership capable of advancing and explaining scientific discovery across all domains.