---
title: Explanation Purposes and Types
url: https://www.emergentmind.com/topics/explanation-purposes-and-types
type: topic
---

# Explanation Purposes and Types

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 [2003.07523][2010.01479]:

| 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 [2106.11988] 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. [2404.16644] 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 [2311.09020]:

1. Rule/principle
2. Similar example
3. Scenario/condition
4. Future implications
5. Personal preference
6. Status/root cause
7. 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 [2206.04438].

## 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 [2205.15406] 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 [2106.13976] 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 [2006.08672] 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 [2012.07179]:

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

**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) [2304.14760].

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) [2206.04438][2010.01479].

**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 [2503.19237][2508.14417].

## 4. Criteria for Classification and Selection

Explanations are classified/tailored by several orthogonal axes:

- **Content focus:** User action, system behavior, terminology, UI, or privacy [2404.16644].
- **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) [2206.04438].
- **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 [2010.01479][2003.07523].
- **Rule-extraction, attribution, and intrinsic methods** in deep learning map onto different explanation purposes and user types (experts vs. lay users) [1803.07517].
- **Recourse** is reframed as a recommendation task, not explanation proper, with diversity and capability constraints [2205.15406][2402.10762].

### 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 [2404.16644].
- Explanations are crucial for compliance (e.g., GDPR) and operational transparency [2206.04438].

### 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 [2508.14417][2503.19237].
- **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) [2402.10762].

## 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 [2003.07523][2010.01479].
- **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 [2010.01479][2206.04438].
- **Evaluation protocols:** Objective assessment of explanation quality for distinct purposes (trust, error detection, compliance) is largely unsolved [1803.07517][2006.08672].
- **Coverage, parsimony, and completeness:** Especially in high-dimensional models, balancing fidelity, interpretability, and user comprehension is unresolved [1803.07517].
- **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 [2402.10762][2205.15406].
- **Standardization:** There is no universally adopted typology; ontological approaches (e.g., EO, PLEAD) aspire to unify but have not reached consensus deployment [2010.01479][2206.04438].

---

**References:**  
Chari et al. [2003.07523][2010.01479], Tsakalakis et al. [2206.04438], Sullivan & Verreault-Julien [2205.15406], Fragkathoulas et al. [2402.10762], Droste et al. [2404.16644], Zednik [2106.13976], Tan [2106.11988], Nunes & Jannach [2006.08672], Gao et al. [2503.19237][2508.14417], Darwiche et al. [2304.14760], and empirical software/code review work [2311.09020].

Source: https://www.emergentmind.com/topics/explanation-purposes-and-types