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The Role of Human Knowledge in Explainable AI
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/ail2.49openalex-snapshot
How level of explanation detail affects human performance in interpretable intelligent systems: A study on explainable fact checking
inferredcites →https://doi.org/10.1002/ail2.60openalex-snapshot
Reframing explanation as an interactive medium: The EQUAS (Explainable QUestion Answering System) project
inferredcites →https://doi.org/10.1007/978-3-030-57321-8_24openalex-snapshot
Calibrating Human-AI Collaboration: Impact of Risk, Ambiguity and Transparency on Algorithmic Bias
inferredcites →https://doi.org/10.1007/978-3-030-60117-1_33openalex-snapshot
Human-Centered Explainable AI: Towards a Reflective Sociotechnical Approach
inferredcites →https://doi.org/10.1007/978-3-030-75015-2_14openalex-snapshot
Data-Debugging Through Interactive Visual Explanations
inferredcites →https://doi.org/10.1007/978-3-030-84060-0_20openalex-snapshot
Human-in-the-Loop Model Explanation via Verbatim Boundary Identification in Generated Neighborhoods
inferredcites →https://doi.org/10.1007/978-3-030-86517-7_27openalex-snapshot
Crowdsourcing Evaluation of Saliency-Based XAI Methods
inferredcites →https://doi.org/10.1007/s13218-020-00632-3openalex-snapshot
eXplainable Cooperative Machine Learning with NOVA
inferredcites →https://doi.org/10.1016/j.artint.2021.103456openalex-snapshot
Show or suppress? Managing input uncertainty in machine learning model explanations
inferredcites →https://doi.org/10.1016/j.artint.2021.103471openalex-snapshot
Using ontologies to enhance human understandability of global post-hoc explanations of black-box models
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.2021.05.008openalex-snapshot
A review of uncertainty quantification in deep learning: Techniques, applications and challenges
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.knosys.2021.106916openalex-snapshot
Classification by ordinal sums of conjunctive and disjunctive functions for explainable AI and interpretable machine learning solutions
inferredcites →https://doi.org/10.1016/j.procs.2018.05.062openalex-snapshot
Crowdsourcing – A Step Towards Advanced Machine Learning
inferredcites →https://doi.org/10.1109/bigdata50022.2020.9378459openalex-snapshot
Human-in-the-loop Approach towards Dual Process AI Decisions
inferredcites →https://doi.org/10.1109/iccv.2017.74openalex-snapshot
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
inferredcites →https://doi.org/10.1109/ice/itmc52061.2021.9570236openalex-snapshot
Moving from ‘black box’ to ‘glass box’ Artificial Intelligence in Manufacturing with XMANAI
inferredcites →https://doi.org/10.1109/iv53921.2021.00035openalex-snapshot
Context-Sensitive Visualization of Deep Learning Natural Language Processing Models
inferredcites →https://doi.org/10.1109/pacificvis48177.2020.7090openalex-snapshot
ExplainExplore: Visual Exploration of Machine Learning Explanations
inferredcites →https://doi.org/10.1109/smc42975.2020.9283240openalex-snapshot
Human-In-The-Loop Construction of Decision Tree Classifiers with Parallel Coordinates
inferredcites →https://doi.org/10.1109/taffc.2020.3043603openalex-snapshot
Unraveling ML Models of Emotion With NOVA: Multi-Level Explainable AI for Non-Experts
inferredcites →https://doi.org/10.1109/tvcg.2018.2864769openalex-snapshot
Visual Analytics for Topic Model Optimization based on User-Steerable Speculative Execution
inferredcites →https://doi.org/10.1109/tvcg.2019.2934629openalex-snapshot
explAIner: A Visual Analytics Framework for Interactive and Explainable Machine Learning
inferredcites →https://doi.org/10.1109/tvcg.2021.3114837openalex-snapshot
Human-in-the-loop Extraction of Interpretable Concepts in Deep Learning Models
inferredcites →https://doi.org/10.1109/visual.2019.8933695openalex-snapshot
TeleGam: Combining Visualization and Verbalization for Interpretable Machine Learning
inferredcites →https://doi.org/10.1109/wacv.2018.00097openalex-snapshot
Grad-CAM++: Generalized Gradient-Based Visual Explanations for Deep Convolutional Networks
inferredcites →https://doi.org/10.1145/3236009openalex-snapshot
A Survey of Methods for Explaining Black Box Models
inferredcites →https://doi.org/10.1145/3334480.3382831openalex-snapshot
Getting Playful with Explainable AI: Games with a Purpose to Improve Human Understanding of AI
inferredcites →https://doi.org/10.1145/3377325.3377480openalex-snapshot
How do visual explanations foster end users' appropriate trust in machine learning?
inferredcites →https://doi.org/10.1145/3397481.3450689openalex-snapshot
Quantitative Evaluation of Machine Learning Explanations: A Human-Grounded Benchmark
inferredcites →https://doi.org/10.1145/3411764.3445315openalex-snapshot
Manipulating and Measuring Model Interpretability
inferredcites →https://doi.org/10.1145/3429448openalex-snapshot
QuestionComb: A Gamification Approach for the Visual Explanation of Linguistic Phenomena through Interactive Labeling
inferredcites →https://doi.org/10.1145/3442381.3450069openalex-snapshot
What do You Mean? Interpreting Image Classification with Crowdsourced Concept Extraction and Analysis
inferredcites →https://doi.org/10.1145/3449213openalex-snapshot
Crowdsourcing and Evaluating Concept-driven Explanations of Machine Learning Models
inferredcites →https://doi.org/10.1145/3462757.3466059openalex-snapshot
Discovering the rationale of decisions
inferredcites →https://doi.org/10.1145/3485447.3512040openalex-snapshot
What Should You Know? A Human-In-the-Loop Approach to Unknown Unknowns Characterization in Image Recognition
inferredcites →https://doi.org/10.1145/3485447.3512241openalex-snapshot
Ready Player One! Eliciting Diverse Knowledge Using A Configurable Game
inferredcites →https://doi.org/10.1145/3490099.3511111openalex-snapshot
Building Trust in Interactive Machine Learning via User Contributed Interpretable Rules
inferredcites →https://doi.org/10.1145/3491102.3517474openalex-snapshot
How can Explainability Methods be Used to Support Bug Identification in Computer Vision Models?
inferredcites →https://doi.org/10.1145/3491102.3517551openalex-snapshot
Interpretable Directed Diversity: Leveraging Model Explanations for Iterative Crowd Ideation
inferredcites →https://doi.org/10.1145/3491102.3517650openalex-snapshot
Improving understandability of feature contributions in model-agnostic explainable AI tools
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/10.1145/3593013.3593970openalex-snapshot
Machine Explanations and Human Understanding
inferredcites →https://doi.org/10.1145/3625240openalex-snapshot
XAutoML: A Visual Analytics Tool for Understanding and Validating Automated Machine Learning
inferredcites →https://doi.org/10.1177/0165551512437638openalex-snapshot
Towards an integrated crowdsourcing definition
inferredcites →https://doi.org/10.1186/s13643-021-01626-4openalex-snapshot
The PRISMA 2020 statement: an updated guideline for reporting systematic reviews
inferredcites →https://doi.org/10.1609/aaai.v33i01.33012438openalex-snapshot
Human-in-the-Loop Feature Selection
inferredcites →https://doi.org/10.1609/aaai.v35i7.16734openalex-snapshot
MARTA: Leveraging Human Rationales for Explainable Text Classification
inferredcites →https://doi.org/10.1609/hcomp.v6i1.13337openalex-snapshot
Towards Accountable AI: Hybrid Human-Machine Analyses for Characterizing System Failure
inferredcites →https://doi.org/10.1609/hcomp.v7i1.5280openalex-snapshot
Human Evaluation of Models Built for Interpretability
inferredcites →https://doi.org/10.1609/hcomp.v8i1.7469openalex-snapshot
The Role of Domain Expertise in User Trust and the Impact of First Impressions with Intelligent Systems
inferredcites →https://doi.org/10.1609/hcomp.v8i1.7477openalex-snapshot
How Useful Are the Machine-Generated Interpretations to General Users? A Human Evaluation on Guessing the Incorrectly Predicted Labels
inferredcites →https://doi.org/10.1609/hcomp.v9i1.18938openalex-snapshot
From Human Explanation to Model Interpretability: A Framework Based on Weight of Evidence
inferredcites →https://doi.org/10.1609/icaps.v32i1.19860openalex-snapshot
VizXP: A Visualization Framework for Conveying Explanations to Users in Model Reconciliation Problems
inferredcites →https://doi.org/10.18653/v1/2020.acl-main.408openalex-snapshot
ERASER: A Benchmark to Evaluate Rationalized NLP Models
inferredcites →https://doi.org/10.18653/v1/2020.acl-main.491openalex-snapshot
Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior?
inferredcites →https://doi.org/10.18653/v1/2020.emnlp-main.24openalex-snapshot
FIND: Human-in-the-Loop Debugging Deep Text Classifiers
inferredcites →https://doi.org/10.18653/v1/2020.emnlp-main.575openalex-snapshot
F1 is Not Enough! Models and Evaluation Towards User-Centered Explainable Question Answering
inferredcites →https://doi.org/10.18653/v1/2021.blackboxnlp-1.3openalex-snapshot
Does External Knowledge Help Explainable Natural Language Inference? Automatic Evaluation vs. Human Ratings
inferredcites →https://doi.org/10.18653/v1/2021.naacl-industry.22openalex-snapshot
When and Why a Model Fails? A Human-in-the-loop Error Detection Framework for Sentiment Analysis
inferredcites →https://doi.org/10.18653/v1/2022.acl-long.249openalex-snapshot
How Do We Answer Complex Questions: Discourse Structure of Long-form Answers
inferredcites →https://doi.org/10.18653/v1/2022.findings-acl.75openalex-snapshot
Using Interactive Feedback to Improve the Accuracy and Explainability of Question Answering Systems Post-Deployment
inferredcites →https://doi.org/10.18653/v1/d18-1537openalex-snapshot
Interpreting Recurrent and Attention-Based Neural Models: a Case Study on Natural Language Inference
inferredcites →https://doi.org/10.18653/v1/n18-1097openalex-snapshot
Comparing Automatic and Human Evaluation of Local Explanations for Text Classification
inferredcites →https://doi.org/10.18653/v1/w19-4807openalex-snapshot
Do Human Rationales Improve Machine Explanations?
inferredcites →https://doi.org/10.2139/ssrn.3303123openalex-snapshot
Transparent Artificial Intelligence – A Conceptual Framework for Evaluating AI-based Clinical Decision Support Systems
inferredcites →https://doi.org/10.2139/ssrn.3872711openalex-snapshot
Expl(Ai)Ned: The Impact of Explainable Artificial Intelligence on Cognitive Processes
inferredcites →https://doi.org/10.24963/ijcai.2020/742openalex-snapshot
Generating Natural Counterfactual Visual Explanations
inferredcites →https://doi.org/10.3389/frai.2022.826499openalex-snapshot
EXP-Crowd: A Gamified Crowdsourcing Framework for Explainability
inferredcites →https://doi.org/10.3390/electronics8080832openalex-snapshot
Machine Learning Interpretability: A Survey on Methods and Metrics
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.2005.13275openalex-snapshot
Who is this Explanation for? Human Intelligence and Knowledge Graphs for eXplainable AI
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/asi.24889openalex-snapshot
Human‐centered explainable artificial intelligence: An Annual Review of Information Science and Technology (ARIST) paper
inferred← citeshttps://doi.org/10.1007/978-3-031-64642-3_8openalex-snapshot
Sustainable Development Goal 14: Explainable AI (XAI) for Ocean Health
inferred← citeshttps://doi.org/10.1007/s10072-025-08781-9openalex-snapshot
Automated vertigo diagnosis using video-oculography and artificial intelligence for clinic-based triage: Preliminary results
inferred← citeshttps://doi.org/10.1007/s13042-024-02396-wopenalex-snapshot
SGSM: semi-generalist sensing model combining handcrafted and deep learning methods
inferred← citeshttps://doi.org/10.1007/s13659-024-00431-5openalex-snapshot
The contemporary nexus of medicines security and bioprospecting: a future perspective for prioritizing the patient
inferred← citeshttps://doi.org/10.1016/b978-0-44-340553-2.00008-3openalex-snapshot
Foundational approaches to post-hoc explainability for image classification
inferred← citeshttps://doi.org/10.1016/b978-0-44-340553-2.00012-5openalex-snapshot
On the principles and effectiveness of gamification in bidirectional artificial intelligence and explainable AI
inferred← citeshttps://doi.org/10.1016/b978-0-443-33584-6.00013-xopenalex-snapshot
AI in diagnostics: Enhancing accuracy and efficiency
inferred← citeshttps://doi.org/10.1016/j.atech.2025.100879openalex-snapshot
A novel application with explainable machine learning (SHAP and LIME) to predict soil N, P, and K nutrient content in cabbage cultivation
inferred← citeshttps://doi.org/10.1016/j.commatsci.2024.113024openalex-snapshot
A novel design for refractory complex concentrated alloys based on multi-objective bi-level optimization
inferred← citeshttps://doi.org/10.1016/j.neunet.2024.106392openalex-snapshot
Human attention guided explainable artificial intelligence for computer vision models
inferred← citeshttps://doi.org/10.1016/j.sciaf.2023.e01718openalex-snapshot
Assessment of machine learning classifiers in mapping the cocoa-forest mosaic landscape of Ghana
inferred← citeshttps://doi.org/10.1016/j.teler.2025.100205openalex-snapshot
Ethical considerations in AI-based user profiling for knowledge management: A critical review
inferred← citeshttps://doi.org/10.1080/15732479.2023.2230564openalex-snapshot
An interpretable model for bridge scour risk assessment using explainable artificial intelligence and engineers’ expertise
inferred← citeshttps://doi.org/10.1080/17483107.2025.2507684openalex-snapshot
Towards inclusive explainable artificial intelligence: a thematic analysis and scoping review on tools for persons with disabilities
inferred← citeshttps://doi.org/10.1093/bib/bbad236openalex-snapshot
Explainable AI for Bioinformatics: Methods, Tools and Applications
inferred← citeshttps://doi.org/10.1109/access.2024.3401547openalex-snapshot
Applications, Challenges, and Future Directions of Human-in-the-Loop Learning
inferred← citeshttps://doi.org/10.1109/access.2024.3428401openalex-snapshot
Emerging Technologies for Sustainable Agriculture: The Power of Humans and the Way Ahead
inferred← citeshttps://doi.org/10.1109/access.2025.3602635openalex-snapshot
Explainable AI for Multivariate Time Series Pattern Exploration: Latent Space Visual Analytics With Temporal Fusion Transformer and Variational Autoencoders in Power Grid Event Diagnosis
inferred← citeshttps://doi.org/10.1109/ic2sdt62152.2024.10696477openalex-snapshot
Towards the Development of eXplainable Digital Twins for Precision Agriculture
inferred← citeshttps://doi.org/10.1109/icicds70526.2026.11604693openalex-snapshot
Explainable AI for Emotion-Aware Humancomputer Interaction Systems
inferred← citeshttps://doi.org/10.1109/iwqos61813.2024.10682922openalex-snapshot
SGSM: A Foundation-model-like Semi-generalist Sensing Model
inferred← citeshttps://doi.org/10.1109/mipro65660.2025.11131766openalex-snapshot
Future Trends in AI for Cybersecurity and Digital Forensics: A Systematic Literature Survey
inferred← citeshttps://doi.org/10.1109/tim.2022.3232159openalex-snapshot
Reduce System Redundancy and Optimize Sensor Disposition for EMG–IMU Multimodal Fusion Human–Machine Interfaces With XAI
inferred← citeshttps://doi.org/10.1145/3581641.3584058openalex-snapshot
It Seems Smart, but It Acts Stupid: Development of Trust in AI Advice in a Repeated Legal Decision-Making Task
inferred← citeshttps://doi.org/10.1145/3623809.3623938openalex-snapshot
Enhancing Explanaibility in AI: Food Recommender System Use Case
inferred← citeshttps://doi.org/10.1145/3686164openalex-snapshot
Understanding Trust and Reliance Development in AI Advice: Assessing Model Accuracy, Model Explanations, and Experiences from Previous Interactions
inferred← citeshttps://doi.org/10.1145/3715966.3715970openalex-snapshot
The Effects of Information and Incentive Interventions on the Adoption of Algorithms in Human Resources: An Experimental Study
inferred← citeshttps://doi.org/10.1177/22104968261431426openalex-snapshot
Semantic Web Technologies in Sensor-Based Personal Health Monitoring Systems: A Systematic Mapping Study
inferred← citeshttps://doi.org/10.17762/ijritcc.v11i11.10063openalex-snapshot
Navigating the Landscape of Robust and Secure Artificial Intelligence: A Comprehensive Literature Review
inferred← citeshttps://doi.org/10.22159/ajpcr.2023.v16i11.48193openalex-snapshot
INTEGRATING ARTIFICIAL INTELLIGENCE IN DISEASE DIAGNOSIS, TREATMENT, AND FORMULATION DEVELOPMENT: A REVIEW
inferred← citeshttps://doi.org/10.22214/ijraset.2024.56885openalex-snapshot
The Role of Explanability in AI-Driven Fashion Recommendation Model - A Review
inferred← citeshttps://doi.org/10.22219/kinetik.v7i4.1535openalex-snapshot
Electronic Medical Record Data Analysis and Prediction of Stroke Disease Using Explainable Artificial Intelligence (XAI)
inferred← citeshttps://doi.org/10.3389/fcomp.2025.1662185openalex-snapshot
Explicating the trust process for effective human interaction with artificial intelligence and machine learning systems
inferred← citeshttps://doi.org/10.34133/space.0255openalex-snapshot
Developing Explainable Artificial Intelligence Models for Space Science Applications
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