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Explainable artificial intelligence: History, Art & Products
Within artificial intelligence (AI), explainable AI (XAI), generally overlapping with interpretable AI or explainable machine learning (XML), is a field of research that explores methods that provide humans with the ability of intellectual oversight over AI algorithms. The main focus is on the reasoning behind the decisions or predictions made by the AI…
The analysis highlights History, Art and Products as prominent areas in the source structure around Explainable artificial intelligence.
Related topics
77
Source areas
8
Connected nodes
85
Extracted relationships
52
Concept neighborhoods
33
Bridge connections
85
What this topic covers Research coverage
Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.
Explainability and interpretability techniques · 24 topics
History and methods · 17 topics
Background · 10 topics
Goals · 9 topics
Explainability in social choice · 6 topics
Overview · 5 topics
Regulation · 4 topics
Criticism · 2 topics
Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.
Key facts & relationships
High-confidence facts extracted from structured source data. Use them as anchors for further research.
Explore all related topics Closing gaps
A structured outline of related entities, concepts and subtopics. Open any item to build a new map centered on it.
Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. Each item opens a new analysis centered on that subject.
Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.
How Explainable artificial intelligence connects Entity context
Entity context groups extracted connections by relationship type. Counts show recurrence in the analyzed source; the examples are research signals, not claims about importance.
The extracted context around Explainable artificial intelligence shows recurring relationship patterns in the source. For example, Explainable artificial intelligence → Accountability, ACM Conference, AI, AI's, Artificial Intelligence, DARPA, Dipankar, Electronics, Explainable AI, Explaining How End-to-End Deep, FAccT, Fairness, Fernando, Global, Knight, Leaders, Learning Steers, Local Interpretation, Making, Mazumdar Another extracted example is Explainable artificial intelligence → AI-based, Artificial Intelligence, As, Digital Republic Act, GDPR, General Data Protection Regulation, However, In, In France, International Joint Conference, It, Loi, République, The, The European Union, United States, Workshop, XAI. Use these groups to spot repeated connection types before inspecting the individual relationships.
Explainable artificial intelligence → AI-based, Artificial Intelligence, As, Digital Republic Act, GDPR, General Data Protection Regulation, However, In, In France, International Joint Conference, It, Loi, République, The, The European Union, United States, Workshop, XAI
Important terminology
Frequent words and multi-word phrases across the lead, headings, infobox and body. Useful for terminology coverage.
Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.
Relationships are represented as Subject → Predicate → Object triples. Confidence describes extraction strength, not the real-world importance of a relationship; lower-confidence rows may be contextual rather than explicit.
TTTA extracted 52 structured relationships around Explainable artificial intelligence. Examples in this analysis include SOPHIE that could act as an → instance of → Research in intelligent tutoring systems resulted in developing systems and Explainable artificial intelligence → related to External links → Leaders. The table shows each extracted connection, where it came from and its confidence.
Subject
Predicate
Object
Confidence
Src
SOPHIE that could act as an
instance of
Research in intelligent tutoring systems resulted in developing systems
0.80
text
Explainable artificial intelligence
related to External links
Leaders
0.60
section
Explainable artificial intelligence
related to External links
AI
0.60
section
Explainable artificial intelligence
related to External links
October
0.60
section
Explainable artificial intelligence
related to External links
World Conference
0.60
section
Explainable artificial intelligence
related to External links
Artificial Intelligence
0.60
section
Explainable artificial intelligence
related to External links
ACM Conference
0.60
section
Explainable artificial intelligence
related to External links
Fairness
0.60
section
Explainable artificial intelligence
related to External links
Accountability
0.60
section
Explainable artificial intelligence
related to External links
Transparency
0.60
section
Explainable artificial intelligence
related to External links
FAccT
0.60
section
Explainable artificial intelligence
related to External links
Mazumdar
0.60
section
No triples extracted yet.
Related concept clusters Concept neighborhoods
Concept neighborhoods group terms that sit near one another in the extracted graph. They are useful for discovering adjacent language and context, but proximity does not by itself imply a factual relationship.
The concept neighborhoods around Explainable artificial intelligence bring nearby vocabulary together. In this analysis, examples include Explainable, Intelligence and Research. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
right to explanation
General
Explanations
Machine
Knowledge
Learning
Reasoning
Rules
Given
May
Interpretability
Users
Explainability
machine learning
Learning
Machine
Explain
Research
Even
Used
Understand
Transparency
Explanation
Interpretability
Xai
System
supervised machine learning
Learning
Machine
Explain
Research
Even
Used
Understand
Transparency
Explanation
Interpretability
Xai
System
algorithms
Decisions
Machine
Transparency
Interpretability
Trust
Learning
Data
Explainability
Used
Explanation
Also
Understand
clinical decision support systems
Transparency
Explanations
Trust
Users
System
Explainability
Understanding
Data
Reasoning
Systems
Used
General
supervised learning
Machine
Explain
Research
Xai
System
Understanding
Even
Explanation
Used
Features
Techniques
Transparency
multitask learning
Machine
Explain
Research
Xai
System
Understanding
Even
Explanation
Used
Features
Techniques
Transparency
dictionary learning
Machine
Explain
Research
Xai
System
Understanding
Even
Explanation
Used
Features
Techniques
Transparency
Connections between topic areas Semantic bridges
Semantic bridges connect parts of the graph that would otherwise be more separate. A bridge is a structural signal in this analysis, not a claim that the two topic areas are equivalent.
For Explainable artificial intelligence, one of the stronger structural bridges in this analysis connects Explainable artificial intelligence with Explainability and interpretability techniques. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.
Number of nodes, edges, triples, density and central hubs. Use it to gauge the size and connectivity of the map.
Explainable artificial intelligence
Nodes86
Edges85
Triples52
Avg. degree1.98
Density0.023256
Components1
Source & methodology
TTTA analyzes the structure around Explainable artificial intelligence to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as History, Art & Products, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
