On the public citation graph

Abstraction, validation , and generalization for explainable artificial intelligence

0 signed · 44 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-1-4471-2804-5_6openalex-snapshot
A Review of Uncertainty in Data Visualization
inferredcites →https://doi.org/10.1016/0010-0285(73)90004-2openalex-snapshot
Perception in chess
inferredcites →https://doi.org/10.1016/j.artint.2018.07.007openalex-snapshot
Explanation in artificial intelligence: Insights from the social sciences
inferredcites →https://doi.org/10.1016/j.artint.2020.103387openalex-snapshot
Explanation in AI and law: Past, present and future
inferredcites →https://doi.org/10.1016/j.inffus.2019.12.012openalex-snapshot
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
inferredcites →https://doi.org/10.1016/s0925-2312(97)90018-7openalex-snapshot
Advances in neural information processing systems 7
inferredcites →https://doi.org/10.1037/a0014474openalex-snapshot
Using icon arrays to communicate medical risks: Overcoming low numeracy.
inferredcites →https://doi.org/10.1038/s41598-021-89267-4openalex-snapshot
Mitigating belief projection in explainable artificial intelligence via Bayesian teaching
inferredcites →https://doi.org/10.1073/pnas.1907373117openalex-snapshot
The unreasonable effectiveness of deep learning in artificial intelligence
inferredcites →https://doi.org/10.1080/00207543.2019.1612964openalex-snapshot
Logistics 4.0: a systematic review towards a new logistics system
inferredcites →https://doi.org/10.1080/09695958.2020.1857765openalex-snapshot
Unlocking the potential of AI for English law
inferredcites →https://doi.org/10.1080/741942359openalex-snapshot
Expert Chess Memory: Revisiting the Chunking Hypothesis
inferredcites →https://doi.org/10.1101/2021.02.27.433214openalex-snapshot
Confirmation bias optimizes reward learning
inferredcites →https://doi.org/10.1109/access.2018.2870052openalex-snapshot
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
inferredcites →https://doi.org/10.1109/cvpr.2019.00880openalex-snapshot
Learning to Explain With Complemental Examples
inferredcites →https://doi.org/10.1111/cogs.12613openalex-snapshot
Generalized Information Theory Meets Human Cognition: Introducing a Unified Framework to Model Uncertainty and Information Search
inferredcites →https://doi.org/10.1117/12.2585967openalex-snapshot
Explainable AI for medical imaging: explaining pneumothorax diagnoses with Bayesian teaching
inferredcites →https://doi.org/10.1126/science.1191181openalex-snapshot
Visualizing Uncertainty About the Future
inferredcites →https://doi.org/10.1136/bmj.327.7417.741openalex-snapshot
Simple tools for understanding risks: from innumeracy to insight
inferredcites →https://doi.org/10.1145/2939672.2939778openalex-snapshot
"Why Should I Trust You?"
inferredcites →https://doi.org/10.1145/3173574.3174156openalex-snapshot
Trends and Trajectories for Explainable, Accountable and Intelligible Systems
inferredcites →https://doi.org/10.1145/3233231openalex-snapshot
The Mythos of Model Interpretability
inferredcites →https://doi.org/10.1177/0272989x10369000openalex-snapshot
Do Icon Arrays Help Reduce Denominator Neglect?
inferredcites →https://doi.org/10.1371/journal.pcbi.1005684openalex-snapshot
Confirmation bias in human reinforcement learning: Evidence from counterfactual feedback processing
inferredcites →https://doi.org/10.1559/1523040054738936openalex-snapshot
Visualizing Geospatial Information Uncertainty: What We Know and What We Need to Know
inferredcites →https://doi.org/10.23919/mipro.2018.8400040openalex-snapshot
Explainable artificial intelligence: A survey
inferredcites →https://doi.org/10.24251/hicss.2021.156openalex-snapshot
Reviewing the Need for Explainable Artificial Intelligence (xAI)
inferredcites →https://doi.org/10.4103/jfmpc.jfmpc_440_19openalex-snapshot
Overview of artificial intelligence in medicine
inferredcites →https://doi.org/10.4230/oasics.dx.2024.27openalex-snapshot
On a Method to Measure Supervised Multiclass Model’s Interpretability: Application to Degradation Diagnosis (Short Paper)
inferredcites →https://doi.org/10.4324/9781315413570openalex-snapshot
Big Data in Cognitive Science
inferredcites →https://doi.org/10.48550/arxiv.1705.07874openalex-snapshot
A Unified Approach to Interpreting Model Predictions
inferredcites →https://doi.org/10.48550/arxiv.1811.09720openalex-snapshot
Representer Point Selection for Explaining Deep Neural Networks

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1002/ail2.61openalex-snapshot
DARPA 's explainable AI ( XAI ) program: A retrospective
inferred← citeshttps://doi.org/10.1002/jeo2.12039openalex-snapshot
A practical guide to the implementation of AI in orthopaedic research, Part 6: How to evaluate the performance of AI research?
inferred← citeshttps://doi.org/10.1007/978-3-031-62083-6_34openalex-snapshot
Frugal and Robust AI for Defence Advanced Intelligence
inferred← citeshttps://doi.org/10.1007/978-3-031-77411-9_24openalex-snapshot
Causal Relationships as a Basis for Diagnosis and Decision Making
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2024.108844openalex-snapshot
Exploring explainable AI features in the vocal biomarkers of lung disease
inferred← citeshttps://doi.org/10.1016/j.energy.2026.142240openalex-snapshot
Artificial intelligence-based optimization of HVAC systems: Advances in thermal comfort, energy efficiency, energy consumption, challenges, and research gaps
inferred← citeshttps://doi.org/10.1093/jigpal/jzad006openalex-snapshot
Logics and collaboration
inferred← citeshttps://doi.org/10.1109/iccr67387.2025.11292053openalex-snapshot
Designing an AI-Powered Framework for Detecting Systemic Bias in Judicial Decision-Making Processes Using Legal Text Analytics
inferred← citeshttps://doi.org/10.1109/tiv.2024.3370740openalex-snapshot
A Sequential Metamorphic Testing Framework for Understanding Autonomous Vehicle's Decisions
inferred← citeshttps://doi.org/10.1145/3769678openalex-snapshot
Designing, Implementing, and Evaluating AI Explanations: A Scoping Review of Explainable AI Frameworks
inferred← citeshttps://doi.org/10.3390/systems13020131openalex-snapshot
An AI-Based Decision Support System Utilizing Bayesian Networks for Judicial Decision-Making
inferred← citeshttps://doi.org/10.4018/978-1-6684-3791-9.ch011openalex-snapshot
Basic Issues and Challenges on Explainable Artificial Intelligence (XAI) in Healthcare Systems
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.