Explanation Purposes and Types

Updated 10 January 2026

Explanation Purposes and Types is a framework that defines structured rationales—mechanisms, rules, and evidence—that answer why events, decisions, and actions occur.
It categorizes explanations into types like case-based, contrastive, and counterfactual, detailing methodological approaches and user-focused applications in various domains.
The topic synthesizes formal models and classification criteria, highlighting practical implications for fairness, recourse, adaptive systems, and multi-modal explanation delivery.

Explanations are structured accounts—rationales, mechanisms, rules, or supporting evidence—that answer “why” questions about events, phenomena, decisions, or actions. In AI, software engineering, mathematics education, and decision support, explanation types are diverse and often organized taxonomically to map between purposes (the “why”) and forms of explanation (the “how” and “what”). The following entry synthesizes contemporary taxonomies, formal underpinnings, classification criteria, and domain-specific instantiations of explanation purposes and types.

1. Core Taxonomies of Explanation Types

Taxonomies of explanation types are prevalent across AI, human-computer interaction, education, and software engineering. The leading frameworks distinguish explanation types by their structure, purpose, and knowledge base.

Nine-type AI/Knowledge-Enabled Systems Taxonomy:

Chari et al. enumerate nine distinct types, each serving specific user-oriented purposes (Chari et al., 2020, Chari et al., 2020):

Explanation Type	Core Definition	Primary Purpose
Case-based	Analogy to prior cases/instances	Trust via precedent, analogical reasoning
Contextual	Information about situational/environmental context	Relevance to user’s broader situation
Contrastive	Comparison to a foil/alternative outcome	Decisive difference-spotting (“why A not B?”)
Counterfactual	“What if” with altered inputs or causes	Causal reasoning, sensitivity analysis
Everyday	Common-sense, intuitive narrative	Mental model alignment for lay users
Scientific	Reference to scientific evidence or mechanisms	Domain-level rigor and justification
Simulation-based	“Play through” future or alternative scenarios	Scenario planning, operational foresight
Statistical	Likelihoods, frequencies, or probabilistic evidence	Calibration, quantitative trust
Trace-based	Stepwise inference/provenance of reasoning	Provenance, debugging, audit

Human Explanation Types in NLP:

Tan (Tan, 2021) distinguishes:

Proximal mechanism: Partial, causal/logical chain connecting input to label.
Evidence: Selection of salient input elements supporting the outcome.
Procedure: Explicit executable sequence (decision rules or algorithms).

Software System Explainability Taxonomy:

Droste et al. (Droste et al., 2024) provide five orthogonal types aligned with user needs:

Interaction (operation, navigation, tutorial)
System Behavior (unexpected behavior, bugs, algorithm, consequences)
Domain Knowledge (terminology, system-specific elements)
Privacy & Security (privacy, security)
User Interface

Code Review Explanations:

Seven types structurally recur in collaborative review (Widyasari et al., 2023):

Rule/principle
Similar example
Scenario/condition
Future implications
Personal preference
Status/root cause
Benefit of suggestion

Regulatory and Compliance Dimensions:

Tsakalakis et al. introduce a nine-dimensional ontology for explainability-by-design, including source, perspective (ex ante/ex post), autonomy, trigger, content, scope, explainability goal, recipient, and priority (Tsakalakis et al., 2022).

2. Fundamental Purposes of Explanations

Explanation purposes are systematically distinguished in the literature, often separating mechanistic, justificatory, and actionable aims:

Tripartite Model for Algorithmic Decisions:

Sullivan & Verreault-Julien (Sullivan et al., 2022) and Wachter et al. identify three user-facing functions:

Understanding/Trust: Facilitate insight into the system’s operation or underlying logic.
Contestation: Provide rationales for challenging or appealing a decision.
Recourse: Suggest actionable, achievable changes users might take to reverse an unfavorable outcome.

Each purpose imposes distinct constraints: for understanding, fidelity and completeness are valued; for contestation, legal/ethical grounds must be foregrounded; for recourse, recommendations must lie within the user’s actionable capability set.

Meta-Taxonomies in XAI:

Zednik (Yao, 2021) proposes four axes:

Diagnostic-explanation: Expose specific mechanistic factors for a given output.
Explication-explanation: Render particular outputs human-understandable (social, presentational).
Expectation-explanation: Articulate stable, general behaviors or guarantees.
Role-explanation: Justify/critique the model’s position in a social-technical system.

Decision Support and Recommendation:

Nunes & Jannach (Nunes et al., 2020) stratify explanation purposes hierarchically:

Stakeholder goals: Acceptance, education, use intention, quality improvement.
User-perceived quality: Confidence, transparency, trust, usefulness, scrutability.
Immediate purposes: Effectiveness, efficiency, persuasiveness, transparency.

3. Formal and Theoretical Underpinnings

Philosophy of Science:

Specification-driven frameworks treat explanation as context-sensitive answers to contrastive why-questions, parameterized by a relevance relation R (Naik et al., 2020):

$\sigma : \text{Model} \times \text{Query} \times \text{Specification} \rightarrow \text{Explanation}$

Logic-Based Definitions:

In symbolic classifier analysis, explanations are primes (implicants or implicates) of the “complete reason” formula for an instance:

Sufficient reason (abductive/PI-explanation): Minimal set of feature-value assignments that guarantee the outcome (prime implicant).
Necessary reason (contrastive explanation): Minimal set of properties whose violation flips the decision (prime implicate) (Ji et al., 2023).

For non-binary features, generalized quantification constructs more expressive sufficient/necessary reasons beyond singleton state literals.

Ontological Modeling:

Explanation ontologies formalize types, sources, and properties using DL and OWL2: each explanation instance is a nine-tuple (source, perspective, autonomy, trigger, content, scope, goal, recipient, priority) (Tsakalakis et al., 2022, Chari et al., 2020).

Educational Theories:

In mathematics education, explanation type connects to cognitive theory:

Self-explanation: Schema construction, cognitive load theory.
Peer-explanation: Social presence, collaborative negotiation.
Explanation to fictitious others: Teaching expectancy, generative learning (Gao et al., 25 Mar 2025, Gao et al., 20 Aug 2025).

4. Criteria for Classification and Selection

Explanations are classified/tailored by several orthogonal axes:

Content focus: User action, system behavior, terminology, UI, or privacy (Droste et al., 2024).
User goal: Task accomplishment, understanding, trust calibration, risk mitigation.
Knowledge type: Empirical, theoretical, analogical, procedural, statistical.
Temporal perspective: Ex ante (before outcome), ex post (after outcome) (Tsakalakis et al., 2022).
Responsiveness: Static/manual, adaptive/context-aware.
Recipient: Data subject, regulator, administrator, analyst.
Regulatory trigger/priority: Mandatory vs. discretionary disclosure.

5. Domain-Specific Instantiations

5.1 Explainable AI and Decision Support

Case-based, counterfactual, and trace-based explanations predominate in user-facing XAI systems (Chari et al., 2020, Chari et al., 2020).
Rule-extraction, attribution, and intrinsic methods in deep learning map onto different explanation purposes and user types (experts vs. lay users) (Ras et al., 2018).
Recourse is reframed as a recommendation task, not explanation proper, with diversity and capability constraints (Sullivan et al., 2022, Fragkathoulas et al., 2024).

5.2 Software Systems

Interaction explanations dominate feature-rich software and productivity tools, while system behavior and privacy/security explanations are prominent in consumer and safety-critical systems (Droste et al., 2024).
Explanations are crucial for compliance (e.g., GDPR) and operational transparency (Tsakalakis et al., 2022).

5.3 Mathematics and Statistics Education

Self-explanation (SE) and peer explanation (PE) are distinguished by generativity, social feedback, and procedural structuring; efficacy depends on context, prompt specificity, and group composition (Gao et al., 20 Aug 2025, Gao et al., 25 Mar 2025).
Explanation to fictitious others enables teaching expectancy effects with mitigated peer-dynamics.

5.4 Fairness and Responsible AI

Explanations for fairness operate at three levels: measurement (e.g., burden via counterfactual distance), causal diagnosis (e.g., Shapley or path decomposition), and designing mitigation (e.g., recourse optimization under fairness constraints) (Fragkathoulas et al., 2024).

6. Relationships, Overlaps, and Hybridization

Distinct explanation types often hybridize in practice. For instance:

Contrastive vs. counterfactual: Both address “why not” but differ: contrastive is outcome-focused, counterfactual is input/causal-focused (Chari et al., 2020, Chari et al., 2020).
Case-based and simulation-based: Empirical precedent vs. hypothetical scenario.
Trace-based and statistical: Stepwise vs. aggregate rationale.
Many deployed systems blend trace, statistical, scientific, and everyday explanations to address a spectrum of user needs and contexts.

7. Open Challenges and Future Directions

Granularity & tailoring: Adaptive, user-specific selection of explanation type remains challenging, requiring automated mapping from user profile and context to optimal explanation forms (Chari et al., 2020, Tsakalakis et al., 2022).
Evaluation protocols: Objective assessment of explanation quality for distinct purposes (trust, error detection, compliance) is largely unsolved (Ras et al., 2018, Nunes et al., 2020).
Coverage, parsimony, and completeness: Especially in high-dimensional models, balancing fidelity, interpretability, and user comprehension is unresolved (Ras et al., 2018).
Multi-modal/hybrid explanations: Integrating textual, visual, statistical, and analogical elements for layered explanation delivery.
Fairness and recourse: Developing explanation-driven fairness metrics and ensuring actionable, equitable recourse under practical constraints (Fragkathoulas et al., 2024, Sullivan et al., 2022).
Standardization: There is no universally adopted typology; ontological approaches (e.g., EO, PLEAD) aspire to unify but have not reached consensus deployment (Chari et al., 2020, Tsakalakis et al., 2022).