On the public citation graph

Legal XAI: a Systematic Review and Interdisciplinary Mapping of XAI and EU Law, Towards a Research Agenda for Legally Responsible AI

https://doi.org/10.2139/ssrn.5371124
0 signed · 108 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 See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.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/10.1002/aisy.202400304openalex-snapshot
A Perspective on Explainable Artificial Intelligence Methods: SHAP and LIME
inferredcites →https://doi.org/10.1007/978-3-030-28954-6_10openalex-snapshot
Layer-Wise Relevance Propagation: An Overview
inferredcites →https://doi.org/10.1007/978-3-030-58986-8_9openalex-snapshot
An Empirical Investigation of the Right to Explanation Under GDPR in Insurance
inferredcites →https://doi.org/10.1007/978-3-031-44064-9_2openalex-snapshot
Perlocution vs Illocution: How Different Interpretations of the Act of Explaining Impact on the Evaluation of Explanations and XAI
inferredcites →https://doi.org/10.1007/s10115-022-01756-8openalex-snapshot
Interpretable deep learning: interpretation, interpretability, trustworthiness, and beyond
inferredcites →https://doi.org/10.1007/s10462-021-10088-yopenalex-snapshot
Explainable artificial intelligence: a comprehensive review
inferredcites →https://doi.org/10.1007/s10462-022-10304-3openalex-snapshot
Explainable AI for clinical and remote health applications: a survey on tabular and time series data
inferredcites →https://doi.org/10.1007/s10462-024-10916-xopenalex-snapshot
Explainable Generative AI (GenXAI): a survey, conceptualization, and research agenda
inferredcites →https://doi.org/10.1007/s10506-020-09270-4openalex-snapshot
Legal requirements on explainability in machine learning
inferredcites →https://doi.org/10.1007/s10618-022-00867-8openalex-snapshot
A comprehensive taxonomy for explainable artificial intelligence: a systematic survey of surveys on methods and concepts
inferredcites →https://doi.org/10.1007/s10618-022-00872-xopenalex-snapshot
Explanatory artificial intelligence (YAI): human-centered explanations of explainable AI and complex data
inferredcites →https://doi.org/10.1007/s10664-025-10645-xopenalex-snapshot
Simplifying software compliance: AI technologies in drafting technical documentation for the AI Act
inferredcites →https://doi.org/10.1007/s10994-023-06479-7openalex-snapshot
Explainable reinforcement learning (XRL): a systematic literature review and taxonomy
inferredcites →https://doi.org/10.1007/s10994-024-06550-xopenalex-snapshot
Evaluating feature attribution methods in the image domain
inferredcites →https://doi.org/10.1007/s11263-019-01228-7openalex-snapshot
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
inferredcites →https://doi.org/10.1007/s12243-022-00926-7openalex-snapshot
Explainable artificial intelligence for cybersecurity: a literature survey
inferredcites →https://doi.org/10.1007/s12559-023-10179-8openalex-snapshot
Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence
inferredcites →https://doi.org/10.1007/s44206-023-00081-zopenalex-snapshot
Explainable AI and Law: An Evidential Survey
inferredcites →https://doi.org/10.1016/j.cag.2021.09.002openalex-snapshot
A survey of visual analytics for Explainable Artificial Intelligence methods
inferredcites →https://doi.org/10.1016/j.dajour.2023.100230openalex-snapshot
A systematic review of Explainable Artificial Intelligence models and applications: Recent developments and future trends
inferredcites →https://doi.org/10.1016/j.egyai.2022.100169openalex-snapshot
Explainable Artificial Intelligence (XAI) techniques for energy and power systems: Review, challenges and opportunities
inferredcites →https://doi.org/10.1016/j.eswa.2024.124710openalex-snapshot
Survey on Explainable AI: Techniques, challenges and open issues
inferredcites →https://doi.org/10.1016/j.imavis.2020.103968openalex-snapshot
Explaining VQA predictions using visual grounding and a knowledge base
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/j.inffus.2021.05.009openalex-snapshot
Notions of explainability and evaluation approaches for explainable artificial intelligence
inferredcites →https://doi.org/10.1016/j.inffus.2021.07.016openalex-snapshot
Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond
inferredcites →https://doi.org/10.1016/j.inffus.2021.10.007openalex-snapshot
Information fusion as an integrative cross-cutting enabler to achieve robust, explainable, and trustworthy medical artificial intelligence
inferredcites →https://doi.org/10.1016/j.inffus.2021.11.003openalex-snapshot
Counterfactuals and causability in explainable artificial intelligence: Theory, algorithms, and applications
inferredcites →https://doi.org/10.1016/j.inffus.2023.03.008openalex-snapshot
A systematic review of trustworthy and explainable artificial intelligence in healthcare: Assessment of quality, bias risk, and data fusion
inferredcites →https://doi.org/10.1016/j.inffus.2023.101805openalex-snapshot
Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence
inferredcites →https://doi.org/10.1016/j.inffus.2024.102472openalex-snapshot
Explainable AI-driven IoMT fusion: Unravelling techniques, opportunities, and challenges with Explainable AI in healthcare
inferredcites →https://doi.org/10.1016/j.knosys.2025.113042openalex-snapshot
Evaluating the effectiveness of XAI techniques for encoder-based language models
inferredcites →https://doi.org/10.1016/j.neucom.2024.127969openalex-snapshot
US-LIME: Increasing fidelity in LIME using uncertainty sampling on tabular data
inferredcites →https://doi.org/10.1016/j.neucom.2024.128111openalex-snapshot
Explainable artificial intelligence: A survey of needs, techniques, applications, and future direction
inferredcites →https://doi.org/10.1016/j.patter.2024.100971openalex-snapshot
Explainability pitfalls: Beyond dark patterns in explainable AI
inferredcites →https://doi.org/10.1038/538020aopenalex-snapshot
Can we open the black box of AI?
inferredcites →https://doi.org/10.1038/s42256-019-0138-9openalex-snapshot
From local explanations to global understanding with explainable AI for trees
inferredcites →https://doi.org/10.1080/10618600.2014.907095openalex-snapshot
Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional Expectation
inferredcites →https://doi.org/10.1080/13600869.2025.2497633openalex-snapshot
How should an explanation be? A mapping of technical and legal desiderata of explanations for machine learning models
inferredcites →https://doi.org/10.1080/2573234x.2021.1952913openalex-snapshot
A social evaluation of the perceived goodness of explainability in machine learning
inferredcites →https://doi.org/10.1093/idpl/ipx005openalex-snapshot
Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation
inferredcites →https://doi.org/10.1093/oso/9780190088583.001.0001openalex-snapshot
The Brussels Effect: How the European Union Rules the World
inferredcites →https://doi.org/10.1109/34.589207openalex-snapshot
A comparative analysis of methods for pruning decision trees
inferredcites →https://doi.org/10.1109/access.2021.3093456openalex-snapshot
Attention Meets Perturbations: Robust and Interpretable Attention With Adversarial Training
inferredcites →https://doi.org/10.1109/access.2023.3315605openalex-snapshot
A Conceptual Model Framework for XAI Requirement Elicitation of Application Domain System
inferredcites →https://doi.org/10.1109/access.2025.3546681openalex-snapshot
A Literature Review on Applications of Explainable Artificial Intelligence (XAI)
inferredcites →https://doi.org/10.1109/cibcb56990.2023.10264877openalex-snapshot
A Theoretical Framework for AI Models Explainability with Application in Biomedicine
inferredcites →https://doi.org/10.1109/comst.2023.3280465openalex-snapshot
Explainable Intrusion Detection for Cyber Defences in the Internet of Things: Opportunities and Solutions
inferredcites →https://doi.org/10.1109/comst.2024.3437248openalex-snapshot
A Survey on XAI for 5G and Beyond Security: Technical Aspects, Challenges and Research Directions
inferredcites →https://doi.org/10.1109/cvpr.2016.319openalex-snapshot
Learning Deep Features for Discriminative Localization
inferredcites →https://doi.org/10.1109/cvpr52729.2023.01549openalex-snapshot
Shortcomings of Top-Down Randomization-Based Sanity Checks for Evaluations of Deep Neural Network Explanations
inferredcites →https://doi.org/10.1109/cvprw.2017.215openalex-snapshot
Explaining the Unexplained: A CLass-Enhanced Attentive Response (CLEAR) Approach to Understanding Deep Neural Networks
inferredcites →https://doi.org/10.1109/icdm.2019.00036openalex-snapshot
Efficient Data Representation by Selecting Prototypes with Importance Weights
inferredcites →https://doi.org/10.1109/ojcoms.2022.3215676openalex-snapshot
Explainable AI Over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directions
inferredcites →https://doi.org/10.1109/rbme.2022.3185953openalex-snapshot
Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review
inferredcites →https://doi.org/10.1109/satml59370.2024.00039openalex-snapshot
Unifying Corroborative and Contributive Attributions in Large Language Models
inferredcites →https://doi.org/10.1109/tai.2023.3266418openalex-snapshot
A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?
inferredcites →https://doi.org/10.1109/tnsm.2023.3282740openalex-snapshot
A Survey on Explainable Artificial Intelligence for Cybersecurity
inferredcites →https://doi.org/10.1111/rego.12391openalex-snapshot
The right to contest automated decisions under the General Data Protection Regulation : Beyond the so‐called “right to explanation”
inferredcites →https://doi.org/10.1145/3351095.3372850openalex-snapshot
Explaining machine learning classifiers through diverse counterfactual explanations
inferredcites →https://doi.org/10.1145/3375627.3375830openalex-snapshot
Fooling LIME and SHAP
inferredcites →https://doi.org/10.1145/3490099.3511119openalex-snapshot
Investigating Explainability of Generative AI for Code through Scenario-based Design
inferredcites →https://doi.org/10.1145/3544548.3581314openalex-snapshot
On Selective, Mutable and Dialogic XAI: a Review of What Users Say about Different Types of Interactive Explanations
inferredcites →https://doi.org/10.1145/3561048openalex-snapshot
Explainable AI (XAI): Core Ideas, Techniques, and Solutions
inferredcites →https://doi.org/10.1145/3583558openalex-snapshot
From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI
inferredcites →https://doi.org/10.1145/3593013.3594069openalex-snapshot
The role of explainable AI in the context of the AI Act
inferredcites →https://doi.org/10.1145/3593013.3594122openalex-snapshot
Achieving Diversity in Counterfactual Explanations: a Review and Discussion
inferredcites →https://doi.org/10.1145/3616864openalex-snapshot
Explainable Reinforcement Learning: A Survey and Comparative Review
inferredcites →https://doi.org/10.1145/3625287openalex-snapshot
Explainable Deep Learning Methods in Medical Image Classification: A Survey
inferredcites →https://doi.org/10.1145/3639475.3640112openalex-snapshot
An Empirical Study on Compliance with Ranking Transparency in the Software Documentation of EU Online Platforms
inferredcites →https://doi.org/10.1145/3672276openalex-snapshot
Visualization for Recommendation Explainability: A Survey and New Perspectives
inferredcites →https://doi.org/10.1175/aies-d-23-0074.1openalex-snapshot
Finding the Right XAI Method—A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate Science
inferredcites →https://doi.org/10.1177/160940690600500107openalex-snapshot
Demonstrating Rigor Using Thematic Analysis: A Hybrid Approach of Inductive and Deductive Coding and Theme Development
inferredcites →https://doi.org/10.1214/aos/1013203451openalex-snapshot
Greedy function approximation: A gradient boosting machine.
inferredcites →https://doi.org/10.1371/journal.pone.0130140openalex-snapshot
On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation
inferredcites →https://doi.org/10.1609/aaai.v32i1.11491openalex-snapshot
Anchors: High-Precision Model-Agnostic Explanations
inferredcites →https://doi.org/10.1609/aaai.v33i01.33013681openalex-snapshot
Interpretation of Neural Networks Is Fragile
inferredcites →https://doi.org/10.1609/aaai.v36i11.21452openalex-snapshot
Evaluating Explainable AI on a Multi-Modal Medical Imaging Task: Can Existing Algorithms Fulfill Clinical Requirements?
inferredcites →https://doi.org/10.1609/aaai.v38i19.30127openalex-snapshot
Promoting Counterfactual Robustness through Diversity
inferredcites →https://doi.org/10.1609/aies.v7i1.31648openalex-snapshot
How Should AI Decisions Be Explained? Requirements for Explanations from the Perspective of European Law
inferredcites →https://doi.org/10.1609/aimag.v38i3.2741openalex-snapshot
European Union Regulations on Algorithmic Decision Making and a “Right to Explanation”
inferredcites →https://doi.org/10.1609/hcomp.v10i1.21995openalex-snapshot
Connecting Algorithmic Research and Usage Contexts: A Perspective of Contextualized Evaluation for Explainable AI
inferredcites →https://doi.org/10.1613/jair.1.15348openalex-snapshot
Human-in-the-Loop Reinforcement Learning: A Survey and Position on Requirements, Challenges, and Opportunities
inferredcites →https://doi.org/10.18653/v1/n16-3020openalex-snapshot
“Why Should I Trust You?”: Explaining the Predictions of Any Classifier
inferredcites →https://doi.org/10.2139/ssrn.2903469openalex-snapshot
Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation
inferredcites →https://doi.org/10.3233/faia210342openalex-snapshot
A Survey on Methods and Metrics for the Assessment of Explainability Under the Proposed AI Act
inferredcites →https://doi.org/10.3233/faia230367openalex-snapshot
Bridging the Transparency Gap: What Can Explainable AI Learn from the AI Act?
inferredcites →https://doi.org/10.3233/faia240568openalex-snapshot
Aligning XAI with EU Regulations for Smart Biomedical Devices: A Methodology for Compliance Analysis
inferredcites →https://doi.org/10.32604/iasc.2024.059429openalex-snapshot
Overfitting in Machine Learning: A Comparative Analysis of Decision Trees and Random Forests
inferredcites →https://doi.org/10.3389/frai.2023.1124553openalex-snapshot
Decision trees: from efficient prediction to responsible AI
inferredcites →https://doi.org/10.3390/app12199423openalex-snapshot
XAI Systems Evaluation: A Review of Human and Computer-Centred Methods
inferredcites →https://doi.org/10.3390/app122010228openalex-snapshot
Transparency of Artificial Intelligence in Healthcare: Insights from Professionals in Computing and Healthcare Worldwide
inferredcites →https://doi.org/10.3390/app13095809openalex-snapshot
Exploring Local Explanation of Practical Industrial AI Applications: A Systematic Literature Review
inferredcites →https://doi.org/10.3390/electronics12051092openalex-snapshot
An Empirical Survey on Explainable AI Technologies: Recent Trends, Use-Cases, and Categories from Technical and Application Perspectives
inferredcites →https://doi.org/10.3390/make3030032openalex-snapshot
Classification of Explainable Artificial Intelligence Methods through Their Output Formats
inferredcites →https://doi.org/10.3390/make5010006openalex-snapshot
XAIR: A Systematic Metareview of Explainable AI (XAI) Aligned to the Software Development Process
inferredcites →https://doi.org/10.3892/etm.2020.8797openalex-snapshot
Interpretable artificial intelligence framework for COVID‑19 screening on chest X‑rays
inferredcites →https://doi.org/10.48550/arxiv.1605.09304openalex-snapshot
Synthesizing the preferred inputs for neurons in neural networks via deep generator networks
inferredcites →https://doi.org/10.48550/arxiv.1608.08974openalex-snapshot
Towards Transparent AI Systems: Interpreting Visual Question Answering Models
inferredcites →https://doi.org/10.48550/arxiv.1704.02685openalex-snapshot
Learning Important Features Through Propagating Activation Differences
inferredcites →https://doi.org/10.48550/arxiv.1705.07874openalex-snapshot
A Unified Approach to Interpreting Model Predictions
inferredcites →https://doi.org/10.48550/arxiv.1711.11279openalex-snapshot
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)
inferredcites →https://doi.org/10.48550/arxiv.1802.07623openalex-snapshot
Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives
inferredcites →https://doi.org/10.48550/arxiv.2010.14592openalex-snapshot
Shapley Flow: A Graph-based Approach to Interpreting Model Predictions
inferredcites →https://doi.org/10.48550/arxiv.2203.06487openalex-snapshot
Evaluating Explainable AI on a Multi-Modal Medical Imaging Task: Can Existing Algorithms Fulfill Clinical Requirements?
inferredcites →https://doi.org/10.48550/arxiv.2211.05667openalex-snapshot
What Makes a Good Explanation?: A Harmonized View of Properties of Explanations
inferredcites →https://doi.org/10.52202/075280-0719openalex-snapshot
Towards Automated Circuit Discovery for Mechanistic Interpretability
inferredcites →https://doi.org/10.7551/mitpress/3729.001.0001openalex-snapshot
Induction
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.