Explanation in artificial intelligence: Insights from the social sciences
2018/10/26 by Tim Miller · 487 citations
Computer Science · Psychology · #Artificial intelligence #Cognition #Cognitive science #Computer science #Epistemology #Explainable Artificial Intelligence (XAI) #Field (mathematics) #Intuition #Machine Learning in Healthcare #Process (computing) #Psychology #Topic Modeling
paper · pdf · doi:10.1016/j.artint.2018.07.007
published in Artificial Intelligence 267, 1-38 (Elsevier BV)
openalex publication_date 2018/10/26 · openalex created_date 2022/10/01 · openalex updated_date 2026/08/05
Citations
Cited by
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- Coherent without Grounding, Grounded without Success: The Bidirectional Coherence Paradox in Artificial Epistemic Agents
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- The Influence of Human-like Appearance on Expected Robot Explanations
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- Financial Fraud Identification and Interpretability Study for Listed Companies Based on Convolutional Neural Network
- Beyond Satisfaction: From Placebic to Actionable Explanations For Enhanced Understandability
- Human Cognitive Biases in Explanation-Based Interaction: The Case of Within and Between Session Order Effect
- A Framework for Causal Concept-based Model Explanations
- Stress-Testing Causal Claims via Cardinality Repairs
- Optimal Comprehensible Targeting
- Beyond the Black Box: A Cognitive Architecture for Explainable and Aligned AI
- Actionable and diverse counterfactual explanations incorporating domain knowledge and causal constraints
- Language-Independent Sentiment Labelling with Distant Supervision: A Case Study for English, Sepedi and Setswana
- Formal Abductive Latent Explanations for Prototype-Based Networks
- Rethinking Saliency Maps: A Cognitive Human Aligned Taxonomy and Evaluation Framework for Explanations
- Efficient Search for Diverse Coherent Explanations
- MACIE: Multi-Agent Causal Intelligence Explainer for Collective Behavior Understanding
- llmSHAP: A Principled Approach to LLM Explainability
- Unlocking the Black Box: A Five-Dimensional Framework for Evaluating Explainable AI in Credit Risk
- T-FIX: Text-Based Explanations with Features Interpretable to eXperts
- Explaining Decisions in ML Models: a Parameterized Complexity Analysis (Part I)
- Retrofitters, pragmatists and activists: Public interest litigation for accountable automated decision-making
- Fair and Explainable Credit-Scoring under Concept Drift: Adaptive Explanation Frameworks for Evolving Populations
- Interpretable Model-Aware Counterfactual Explanations for Random Forest
- Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems
- Towards Transparent Robotic Planning via Contrastive Explanations
- State of the Art in Fair ML: From Moral Philosophy and Legislation to\n Fair Classifiers
- Stop Saying "AI"
- Robustness and trustworthiness in AI: a no-go result from formal epistemology
- Imaginative Thought
- What Questions Should Robots Be Able to Answer? A Dataset of User Questions for Explainable Robotics
- Explainability Requirements as Hyperproperties
- The seven roles of generative AI: Potential & pitfalls in combatting misinformation
- Improving Human Verification of LLM Reasoning through Interactive Explanation Interfaces
- Survey of Multimodal Geospatial Foundation Models: Techniques, Applications, and Challenges
- A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata
- Towards the Formalization of a Trustworthy AI for Mining Interpretable Models explOiting Sophisticated Algorithms
- Human-Centered LLM-Agent System for Detecting Anomalous Digital Asset Transactions
- Design Considerations for Human Oversight of AI: Insights from Co-Design Workshops and Work Design Theory
- Leveraging Association Rules for Better Predictions and Better Explanations
- Discrimination, intelligence artificielle et decisions algorithmiques
- Explainability of Large Language Models: Opportunities and Challenges toward Generating Trustworthy Explanations
- Preliminary Quantitative Study on Explainability and Trust in AI Systems
- Discrimination, artificial intelligence, and algorithmic decision-making
- On the Design and Evaluation of Human-centered Explainable AI Systems: A Systematic Review and Taxonomy
- Argumentation-Based Explainability for Legal AI: Comparative and Regulatory Perspectives
- ABLEIST: Intersectional Disability Bias in LLM-Generated Hiring Scenarios
- Extended Triangular Method: A Generalized Algorithm for Contradiction Separation Based Automated Deduction
- Assessing Policy Updates: Toward Trust-Preserving Intelligent User Interfaces
- From Explainability to Action: A Generative Operational Framework for Integrating XAI in Clinical Mental Health Screening
- Training Feature Attribution for Vision Models
- Towards Meaningful Transparency in Civic AI Systems
- Social studies, technology assessment and the pandemic: a comparative analysis of social studies-based policy advice in PTA institutions in France, Germany and the UK during the COVID-19 crisis
- "Sometimes You Need Facts, and Sometimes a Hug": Understanding Older Adults' Preferences for Explanations in LLM-Based Conversational AI Systems
- Cluster Paths: Navigating Interpretability in Neural Networks
- Semantic Regexes: Auto-Interpreting LLM Features with a Structured Language
- Trust in Transparency: How Explainable AI Shapes User Perceptions
- Reproducibility Study of "XRec: Large Language Models for Explainable Recommendation"
- Does Using Counterfactual Help LLMs Explain Textual Importance in Classification?
- Kantian-Utilitarian XAI: Meta-Explained
- Evaluation Framework for Highlight Explanations of Context Utilisation in Language Models
- Onto-Epistemological Analysis of AI Explanations
- From Facts to Foils: Designing and Evaluating Counterfactual Explanations for Smart Environments
- Human-Centered Evaluation of RAG outputs: a framework and questionnaire for human-AI collaboration
- The Unheard Alternative: Contrastive Explanations for Speech-to-Text Models
- Not All Explanations are Created Equal: Investigating the Pitfalls of Current XAI Evaluation
- Contrastive Concept Importance: Explaining Pairwise Class Decisions Through Automatically Extracted Concept Representations
- Conversable Complexity: Agentic LLM Collectives as Interpretable Substrates
- Toward Human-Centered Explainability: Natural Language Explanations for Anomaly Detection
- La inteligencia artificial explicable y su papel clave en la educación
- Efficient & Correct Predictive Equivalence for Decision Trees
- Looking in the mirror: A faithful counterfactual explanation method for interpreting deep image classification models
- Towards a Transparent and Interpretable AI Model for Medical Image Classifications
- Why Johnny Can't Use Agents: Industry Aspirations vs. User Realities with AI Agents
- Explainability Needs in Agriculture: Exploring Dairy Farmers' User Personas
- Fairness-Aware and Interpretable Policy Learning
- Secure human oversight of AI: Threat modeling in a socio-technical context
- Automated Rationale Generation: A Technique for Explainable AI and its Effects on Human Perceptions
- Progressive Disclosure: Designing for Effective Transparency
- Abduct, Act, Predict: Scaffolding Causal Inference for Automated Failure Attribution in Multi-Agent Systems
- LLMs Don't Know Their Own Decision Boundaries: The Unreliability of Self-Generated Counterfactual Explanations
- Explaining Tournament Solutions with Minimal Supports
- An Interpretable Deep Learning Model for General Insurance Pricing
- Temporal Counterfactual Explanations of Behaviour Tree Decisions
- TED: Teaching AI to Explain its Decisions
- Explainable AI in Deep Learning-Based Prediction of Solar Storms
- Triadic Fusion of Cognitive, Functional, and Causal Dimensions for Explainable LLMs: The TAXAL Framework
- TalkToAgent: A Human-centric Explanation of Reinforcement Learning Agents with Large Language Models
- An Information-Flow Perspective on Explainability Requirements: Specification and Verification
- Can AI systems have free will?
- LLM-Generated Explanations Do Not Suffice for Ultra-Strong Machine Learning
- Model Science: getting serious about verification, explanation and control of AI systems
- Interestingness First Classifiers
- From Checking to Sensemaking: A Caregiver-in-the-Loop Framework for AI-Assisted Task Verification in Dementia Care
- Toward an Interaction-Centered Approach to Robot Trustworthiness
- Rigorous Feature Importance Scores based on Shapley Value and Banzhaf Index
- Informative Post-Hoc Explanations Only Exist for Simple Functions
- Who Benefits from AI Explanations? Towards Accessible and Interpretable Systems
- To Explain Or Not To Explain: An Empirical Investigation Of AI-Based Recommendations On Social Media Platforms
- Beyond Technocratic XAI: The Who, What & How in Explanation Design
- De la innovación a la ética: pautas de uso de la inteligencia artificial en la función legislativa
- From Explainable to Explanatory Artificial Intelligence: Toward a New Paradigm for Human-Centered Explanations through Generative AI
- EICAP: Deep Dive in Assessment and Enhancement of Large Language Models in Emotional Intelligence through Multi-Turn Conversations
- Towards Transparent Ethical AI: A Roadmap for Trustworthy Robotic Systems
- Overcoming Algorithm Aversion with Transparency: Can Transparent Predictions Change User Behavior?
- Evaluating User Experience in Conversational Recommender Systems: A Systematic Review Across Classical and LLM-Powered Approaches
- MArgE: Meshing Argumentative Evidence from Multiple Large Language Models for Justifiable Claim Verification
- An Appraisal-Based Approach to Human-Centred Explanations
- Foundations of Interpretable Models
- MetaExplainer: A Framework to Generate Multi-Type User-Centered Explanations for AI Systems
- Co-Producing AI: Toward an Augmented, Participatory Lifecycle
- Sufficient, Necessary and Complete Causal Explanations in Image Classification
- Distilling Knowledge from Large Language Models: A Concept Bottleneck Model for Hate and Counter Speech Recognition
- PHAX: A Structured Argumentation Framework for User-Centered Explainable AI in Public Health and Biomedical Sciences
- Unifying Post-hoc Explanations of Knowledge Graph Completions
- Hybrid Causal Identification and Causal Mechanism Clustering
- Finding Uncommon Ground: A Human-Centered Model for Extrospective Explanations
- On Explaining Visual Captioning with Hybrid Markov Logic Networks
- Explainability in machine learning: a pedagogical perspective
- Metrics for Explainable AI: Challenges and Prospects
- LEAFAGE: Example-based and Feature importance-based Explanationsfor Black-box ML models
- Complexity of Faceted Explanations in Propositional Abduction
- What can AI do for me: Evaluating Machine Learning Interpretations in Cooperative Play
- Towards Explainability of Machine Learning Models in Insurance Pricing
- LLM-Driven Collaborative Model for Untangling Commits via Explicit and Implicit Dependency Reasoning
- Robust Explanations Through Uncertainty Decomposition: A Path to Trustworthier AI
- Exploiting Constraint Reasoning to Build Graphical Explanations for Mixed-Integer Linear Programming
- Explainable AI for online disinformation detection: Insights from a design science research project
- Argumentation meets matrix factorization: A dual perspective for explainable recommendations
- Anthropomimetic Uncertainty: What Verbalized Uncertainty in Language Models is Missing
- Why this and not that? A Logic-based Framework for Contrastive Explanations
- Searching for actual causes: Approximate algorithms with adjustable precision
- Explainable Hierarchical Deep Learning Neural Networks (Ex-HiDeNN)
- Teaching AI to Explain its Decisions Using Embeddings and Multi-Task Learning
- KEA Explain: Explanations of Hallucinations using Graph Kernel Analysis
- Personalised Explanations in Long-term Human-Robot Interactions
- Human-Centered Explainability in Interactive Information Systems: A Survey
- Effective Explanations for Belief-Desire-Intention Robots: When and What to Explain
- Shaping Scientific Explanations to Expert Perspectives with Persona-Conditioned Reinforcement Learning
- Toward Simple and Robust Contrastive Explanations for Image Classification by Leveraging Instance Similarity and Concept Relevance
- Epitome: Pioneering an Experimental Platform for AI-Social Science Integration
- Machine Learning Interpretability: A Science rather than a tool
- IXAII: An Interactive Explainable Artificial Intelligence Interface for Decision Support Systems
- Beyond Autocomplete: Designing CopilotLens Towards Transparent and Explainable AI Coding Agents
- Relative Explanations for Contextual Problems with Endogenous Uncertainty: An Application to Competitive Facility Location
- Mechanistic Interpretability Needs Philosophy
- Patient-Centred Explainability in IVF Outcome Prediction
- Identifying Explanation Needs: Towards a Catalog of User-based Indicators
- Development of a persuasive User Experience Research (UXR) Point of View for Explainable Artificial Intelligence (XAI)
- TRUST: Transparent, Robust and Ultra-Sparse Trees
- Empathy in Explanation
- Teaching Meaningful Explanations
- Simple Radiology VLLM Test-time Scaling with Thought Graph Traversal
- Perspectives on Explanation Formats From Two Stakeholder Groups in Germany: Software Providers and Dairy Farmers
- Social Scientists on the Role of AI in Research
- Explainability in Context: A Multilevel Framework Aligning AI Explanations with Stakeholder with LLMs
- Interpretable Few-Shot Learning via Linear Distillation
- KERAIA: An Adaptive and Explainable Framework for Dynamic Knowledge Representation and Reasoning
- Modeling Users and Online Communities for Abuse Detection: A Position on Ethics and Explainability
- A psychophysics approach for quantitative comparison of interpretable computer vision models
- SynLang and Symbiotic Epistemology: A Manifesto for Conscious Human-AI Collaboration
- Enhancing Interpretability of Quantum-Assisted Blockchain Clustering via AI Agent-Based Qualitative Analysis
- Interpretable phenotyping of Heart Failure patients with Dutch discharge letters
- Do ATCOs Need Explanations, and Why? Towards ATCO-Centered Explainable AI for Conflict Resolution Advisories
- Human-Centered Human-AI Collaboration (HCHAC)
- A New Approach to Backtracking Counterfactual Explanations: A Unified Causal Framework for Efficient Model Interpretability
- AI Fairness
- Multi-Domain Explainability of Preferences
- Explanation User Interfaces: A Systematic Literature Review
- Principles of Explanation in Human-AI Systems
- Robustness questions the interpretability of graph neural networks: what to do?
- A Necessary Step toward Faithfulness: Measuring and Improving Consistency in Free-Text Explanations
- CRITS: Convolutional Rectifier for Interpretable Time Series Classification
- Emerging categories in scientific explanations
- Integrating Counterfactual Simulations with Language Models for Explaining Multi-Agent Behaviour
- Importance of User Control in Data-Centric Steering for Healthcare Experts
- Reassessing Collaborative Writing Theories and Frameworks in the Age of LLMs: What Still Applies and What We Must Leave Behind
- Let's Get You Hired: A Job Seeker's Perspective on Multi-Agent Recruitment Systems for Explaining Hiring Decisions
- Direct Preference Optimization for Adaptive Concept-based Explanations
- Ranking Free RAG: Replacing Re-ranking with Selection in RAG for Sensitive Domains
- Explaining Unreliable Perception in Automated Driving: A Fuzzy-based Monitoring Approach
- Truth or Twist? Optimal Model Selection for Reliable Label Flipping Evaluation in LLM-based Counterfactuals
- SNAPE-PM: Building and Utilizing Dynamic Partner Models for Adaptive Explanation Generation
- Information Science Principles of Machine Learning: A Causal Chain Meta-Framework Based on Formalized Information Mapping
- Heart2Mind: Human-Centered Contestable Psychiatric Disorder Diagnosis System using Wearable ECG Monitors
- Most General Explanations of Tree Ensembles (Extended Version)
- Concept-Guided Interpretability via Neural Chunking
- A User Study Evaluating Argumentative Explanations in Diagnostic Decision Support
- From explanations to shared understandings of AI
- Rhetorical XAI: Explaining AI's Benefits as well as its Use via Rhetorical Design
- Explaining Autonomous Vehicles with Intention-aware Policy Graphs
- SIM-Shapley: A Stable and Computationally Efficient Approach to Shapley Value Approximation
- Interpretable Event Diagnosis in Water Distribution Networks
- Realistic Counterfactual Explanations for Machine Learning-Controlled Mobile Robots using 2D LiDAR
- Integrating Explainable AI in Medical Devices: Technical, Clinical and Regulatory Insights and Recommendations
- See What I Mean? CUE: A Cognitive Model of Understanding Explanations
- What Do People Want to Know About Artificial Intelligence (AI)? The Importance of Answering End-User Questions to Explain Autonomous Vehicle (AV) Decisions
- The Jiminy Advisor: Moral Agreements among Stakeholders Based on Norms and Argumentation
- Bridging the Dual Nature: How Integrated Explanations Enhance Understanding of Technical Artifacts
- Progressive Explanation Generation for Human-robot Teaming
- "The Human Body is a Black Box": Supporting Clinical Decision-Making with Deep Learning
- Embodied Explainability and Ontological Obstacles: Why We Struggle to Explain the Answers of Large Language Models (LLMs)
- A Definition of Good Explanations and the Challenges Explaining LLM Outputs
- X-BCD: Explainable Sensor-Based Behavioral Change Detection in Smart Home Environments
- Explainability and justification of automatic-decision making: A conceptual framework and a practical application
- A Conversational Approach to Well-being Awareness Creation and Behavioural Intention
- xEEGNet: Towards Explainable AI in EEG Dementia Classification
- A Formal Framework for the Explanation of Finite Automata Decisions
- Interpretability Can Be Actionable
- Causal Stories from Sensor Traces: Auditing Epistemic Overreach in LLM-Generated Personal Sensing Explanations
- Disjunctive and Conjunctive Normal Form Explanations of Clusters Using Auxiliary Information
- Explanation format does not matter; but explanations do -- An Eggsbert study on explaining Bayesian Optimisation tasks
- Examining the Effect of Explanations of AI Privacy Redaction in AI-mediated Interactions
- Feedback by Design: Understanding and Overcoming User Feedback Barriers in Conversational Agents
- Enhancing Cell Counting through MLOps: A Structured Approach for Automated Cell Analysis
- Mitigating Societal Cognitive Overload in the Age of AI: Challenges and Directions
- Contrastive explanation: a structural-model approach
- Investigating Co-Constructive Behavior of Large Language Models in Explanation Dialogues
- Two Means to an End Goal: Connecting Explainability and Contestability in the Regulation of Public Sector AI
- When Life Gives You AI, Will You Turn It Into A Market for Lemons? Understanding How Information Asymmetries About AI System Capabilities Affect Market Outcomes and Adoption
- Designing KRIYA: An AI Companion for Wellbeing Self-Reflection
- The Bouncer Problem: Challenges to Remote Explainability
- A Theory of Diagnostic Interpretation in Supervised Classification
- Interpret-able feedback for AutoML systems
- Towards responsible AI for education: Hybrid human-AI to confront the Elephant in the room
- Causal DAG Summarization (Full Version)
- Probabilistic Stability Guarantees for Feature Attributions
- Shedding Light on Black Box Machine Learning Algorithms: Development of an Axiomatic Framework to Assess the Quality of Methods that Explain Individual Predictions
- Towards Human-Centered Early Prediction Models for Academic Performance in Real-World Contexts
- Decomposed Entailment for Factuality Checking and Hallucination Detection
- In Terms of Explainability: Refining Requirements for Self-Explainable Systems
- Revisiting the attacker's knowledge in inference attacks against Searchable Symmetric Encryption
- A Multi-Layered Research Framework for Human-Centered AI: Defining the Path to Explainability and Trust
- GlyTwin: Digital Twin for Glucose Control in Type 1 Diabetes Through Optimal Behavioral Modifications Using Patient-Centric Counterfactuals
- Explaining Explanations in AI
- Towards an Evaluation Framework for Explainable Artificial Intelligence Systems for Health and Well-being
- Exploring the Effectiveness and Interpretability of Texts in LLM-based Time Series Models
- Expectations, Explanations, and Embodiment: Attempts at Robot Failure Recovery
- Learner models: design, components, structure, and modelling
- Predicting Satisfaction of Counterfactual Explanations from Human Ratings of Explanatory Qualities
- Interactive Explanations for Reinforcement-Learning Agents
- Improving Counterfactual Truthfulness for Molecular Property Prediction through Uncertainty Quantification
- Machine Learning Approach to Inpatient Violence Risk Assessment Using Routinely Collected Clinical Notes in Electronic Health Records. [europepmc]
- A Virtual Counseling Application Using Artificial Intelligence for Communication Skills Training in Nursing Education: Development Study. [europepmc]
- Explainable Artificial Intelligence for Neuroscience: Behavioral Neurostimulation. [europepmc]
- A "Third Wheel" Effect in Health Decision Making Involving Artificial Entities: A Psychological Perspective. [europepmc]
- Interpretability of Input Representations for Gait Classification in Patients after Total Hip Arthroplasty. [europepmc]
- A qualitative research framework for the design of user-centered displays of explanations for machine learning model predictions in healthcare. [europepmc]
- Explainable AI: A Review of Machine Learning Interpretability Methods. [europepmc]
- COVIDScreen: explainable deep learning framework for differential diagnosis of COVID-19 using chest X-rays. [europepmc]
- Sacral acceleration can predict whole-body kinetics and stride kinematics across running speeds. [europepmc]
- Explainable Deep Learning for Personalized Age Prediction With Brain Morphology. [europepmc]
- Principles and Practice of Explainable Machine Learning. [europepmc]
- Explainable Artificial Intelligence for Predictive Modeling in Healthcare. [europepmc]
- Model agnostic generation of counterfactual explanations for molecules. [europepmc]
- Artificial intelligence-enabled decision support in nephrology. [europepmc]
- GANterfactual-Counterfactual Explanations for Medical Non-experts Using Generative Adversarial Learning. [europepmc]
- Predicting the future of neuroimaging predictive models in mental health. [europepmc]
- A Perspective on Explanations of Molecular Prediction Models. [europepmc]
- Advancing Computational Toxicology by Interpretable Machine Learning. [europepmc]
- Deep Learning for Medical Image-Based Cancer Diagnosis. [europepmc]
- Fairness of artificial intelligence in healthcare: review and recommendations. [europepmc]
- Integrating Explainability into Graph Neural Network Models for the Prediction of X-ray Absorption Spectra. [europepmc]
- Quantifying the impact of AI recommendations with explanations on prescription decision making. [europepmc]
- Use of artificial intelligence in critical care: opportunities and obstacles. [europepmc]
- Explainable artificial intelligence in breast cancer detection and risk prediction: A systematic scoping review. [europepmc]
- Ethical and legal considerations in healthcare AI: innovation and policy for safe and fair use. [europepmc]