Explanation in artificial intelligence: Insights from the social sciences
2018/10/26 by Tim Miller · 162 citations
Computer Science · #Explainable Artificial Intelligence (XAI) #Machine Learning in Healthcare #Topic Modeling
paper · doi:10.1016/j.artint.2018.07.007
Citations
Cited by
- Unsafe at any AUC: Unlearned Lessons From Sociotechnical Disasters for Responsible AI
- ConceptCF: Concept-based Counterfactuals for the Explainability of Time Series
- AI Contagion in Social Networks
- Align AI to Dynamic Human-AI Workflows
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- The Dead Salmons of AI Interpretability
- Enhancing bikeshare systems with e-bikes in semi-hilly Cities: Insights from Washington D.C
- Rethinking How AI Embeds and Adapts to Human Values: Challenges and Opportunities
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- From Dyad to Triad: Eliciting XAI Requirements in Stroke Rehabilitation
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- The Influence of Human-like Appearance on Expected Robot Explanations
- STACHE: Local Black-Box Explanations for Reinforcement Learning Policies
- ContextualSHAP : Enhancing SHAP Explanations Through Contextual Language Generation
- 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
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- Optimal Comprehensible Targeting
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- Actionable and diverse counterfactual explanations incorporating domain knowledge and causal constraints
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- Formal Abductive Latent Explanations for Prototype-Based Networks
- Rethinking Saliency Maps: A Cognitive Human Aligned Taxonomy and Evaluation Framework for Explanations
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- 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
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- Towards Transparent Robotic Planning via Contrastive Explanations
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- Imaginative Thought
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- 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
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- 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
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- 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
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- Does Using Counterfactual Help LLMs Explain Textual Importance in Classification?
- Kantian-Utilitarian XAI: Meta-Explained
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- Onto-Epistemological Analysis of AI Explanations
- From Facts to Foils: Designing and Evaluating Counterfactual Explanations for Smart Environments
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- The Unheard Alternative: Contrastive Explanations for Speech-to-Text Models
- Not All Explanations are Created Equal: Investigating the Pitfalls of Current XAI Evaluation
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- 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
- Causal Identification of Sufficient, Contrastive and Complete Feature Sets 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
- Metrics for Explainable AI: Challenges and Prospects
- LEAFAGE: Example-based and Feature importance-based Explanationsfor Black-box ML models
- 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]
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- Integrating Explainability into Graph Neural Network Models for the Prediction of X-ray Absorption Spectra. [europepmc]
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- Ethical and legal considerations in healthcare AI: innovation and policy for safe and fair use. [europepmc]