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"Why Should I Trust You?": Explaining the Predictions of Any Classifier

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“Why Should I Trust You?”: Explaining the Predictions of Any Classifier

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The Mythos of Model Interpretability: In machine learning, the concept of interpretability is both important and slippery.

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The Mythos of Model Interpretability

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How the machine “thinks”: Understanding opacity in machine learning algorithms

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Exploring the influence, implications and challenges of integrating generative artificial intelligence into organizational learning and development

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Synergizing climate, energy, and air quality: Uncovering urban heat island, energy consumption, carbon emission, and air pollution nexus linkages

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Values and inductive risk in machine learning modelling: the case of binary classification models

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Accuracy and Interpretability: Struggling with the Epistemic Foundations of Machine Learning-Generated Medical Information and Their Practical Implications for the Doctor-Patient Relationship

Funer, F. (2022) · Philosophy & Technology · doi:10.1007/s13347-022-00505-7 · Cited by 18

From Explanation to Recommendation: Ethical Standards for Algorithmic Recourse

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On the Justified Use of AI Decision Support in Evidence-Based Medicine: Validity, Explainability, and Responsibility

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A Means-End Account of Explainable Artificial Intelligence

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Epistemic Insights as Design Principles for a Teaching-Learning Module on Artificial Intelligence

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The disruption of grief in the technological niche: The case of deathbots

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Self-supervised learning mechanism for identification of eyelid malignant melanoma in pathologic slides with limited annotation

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An Overview of Deep Learning Algorithms and Their Applications in Neuropsychiatry

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Does AlphaGo actually play Go? Concerning the State Space of Artificial IntelligencePreprint

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Requirements for Tax XAI Under Constitutional Principles and Human Rights

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Regulation of biomarker analysis: what can be translated in the clinic?

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From Explainability to Ineffability?

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The Limits of Value Transparency in Machine Learning

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Justice and the Normative Standards of Explainability in Healthcare

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An interpretable evolutionary extreme gradient boosting algorithm for rock slope stability assessment

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Algorithms Don’t Have A Future: On the Relation of Judgement and Calculation

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Can deep learning revolutionize clinical understanding and diagnosis of optic neuropathy?

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Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research DirectionsPreprint

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Explanations in AI as Claims of Tacit Knowledge

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Explainability, Public Reason, and Medical Artificial Intelligence

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L’intelligence artificielle peut-elle être une innovation responsable ?

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Reloading Process Systems Engineering within Chemical Engineering

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Explanation needs and ethical demands: unpacking the instrumental value of XAI

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Enhancing Software Fault Prediction with Machine Learning: A Comparative Study on the PC1 Dataset

Gunda, S. K. (2024) · Global Conference on Communications and Information Technologies (GCCIT) · doi:10.1109/gccit63234.2024.10862351 · Cited by 30

Beyond English: Considering Language and Culture in Psychological Text Analysis

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Against the opacity, and for a qualitative understanding, of artificially intelligent technologies

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Information provision measures for voice agent product recommendations— The effect of process explanations and process visualizations on fairness perceptions

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A perspective on embracing emerging technologies research for organizational behavior

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Artificial Intelligence and Society: Pros and Cons of the Present, Future Prospects

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Allure of Simplicity

Grote, T. (2023) · Philosophy of Medicine · doi:10.5195/pom.2023.139 · Cited by 11

What Kind of Artificial Intelligence Should We Want for Use in Healthcare Decision-Making Applications?

Wadden, J. J. (2021) · Canadian Journal of Bioethics · doi:10.7202/1077636ar · Cited by 11

Can Robots Do Epidemiology? Machine Learning, Causal Inference, and Predicting the Outcomes of Public Health Interventions

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Artificial intelligence: Explainability, ethical issues and bias

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Towards Trustworthy and Understandable AI: Unraveling Explainability Strategies on Simplifying Algorithms, Appropriate Information Disclosure, and High-level Collaboration

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Effect of Transparency of Algorithmic Performance Management on Proactive Work Behavior: Role of Motivation to Improve Performance and Psychological Ownership

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Transparent internal human-machine interfaces in highly automated shuttles to support the communication of minimal risk maneuvers to the passengers

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Deep learning models and the limits of explainable artificial intelligence

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Cracking the clinical code: A scoping review on mechanistic interpretability in medical report generation

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Experimental Disease Prediction Research on Combining Natural Language Processing and Machine Learning

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Entropy frameworks for urban heat storage can support targeted adaptation strategies

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Using Social Media to Monitor Conflict-Related Migration: A Review of Implications for A.I. Forecasting

Ünver, H. A. (2022) · Social Sciences · doi:10.3390/socsci11090395 · Cited by 11

What drives financial competitiveness of industrial sectors in Visegrad Four countries? Evidence by use of machine learning techniques

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How Values Shape the Machine Learning Opacity Problem

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The paradox of trust in artificial intelligence and its rationale

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Exploring the power of photoplethysmogram matrix for atrial fibrillation detection with integrated explainability

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AI Value Alignment Problem: The Clear and Present Danger

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Layer-Wise Relevance Propagation for Smart-Grid Stability Prediction

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Metaphors of the digital age and the Black Box Problem

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Machine Learning Approaches for Software Fault Diagnosis: Evaluating Decision Tree and KNN Models

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A Guide to Machine Learning Epistemic Ignorance, Hidden Paradoxes, and Other Tensions

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Analogue Models and Universal Machines. Paradigms of Epistemic Transparency in Artificial Intelligence

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An End-to-End Trainable Deep Convolutional Neuro-Fuzzy Classifier

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The Reporting Quality of Machine Learning Studies on Pediatric Diabetes Mellitus: Systematic Review

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SIDEs: Separating Idealization from Deceptive 'Explanations' in xAI

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Hidden in plain sight? Irregularities on statutory forms and electoral fraud

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Artificial Intelligence in Perioperative Medicine: A Proposed Common Language With Applications to FDA-Approved Devices

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Recommender Systems: Legal and Ethical Issues

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Understanding with Toy Surrogate Models in Machine Learning

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Percentages and reasons: AI explainability and ultimate human responsibility within the medical field

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AI Art Perceptions with GenFrame – an Image Generating Picture Frame

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Modern Methods and Prospects for Using Artificial Intelligence in Disease Diagnostics: A Narrative Review

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People Attribute Purpose to Autonomous Vehicles When Explaining Their Behavior: Insights from Cognitive Science for Explainable AI

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Assessing the standard-essentiality of 5G technology patents by means of generative artificial intelligence

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Custom emoji based emotion recognition system for dynamic business webpages

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Partial Correlation Analysis and Neural-Network-Based Prediction Model for Biochemical Recurrence of Prostate Cancer after Radical Prostatectomy

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Justificatory explanations in machine learning: for increased transparency through documenting how key concepts drive and underpin design and engineering decisions

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The epistemological foundations of data science: a critical analysisPreprint

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Digital Assessment in Higher Education: Sustainable Trends and Emerging Frontiers in the AI Era

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A moving target in AI-assisted decision-making: dataset shift, model updating, and the problem of update opacity

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Explainable artificial intelligence for natural language processing: A survey

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Empowering Explainable Artificial Intelligence Through Case-Based Reasoning: A Comprehensive Exploration

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How Neurons in Deep Models Relate with Neurons in the Brain

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The concept of responsible artificial intelligence as the future of artificial intelligence in medicine

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Optimizing the Clinical Direction of Artificial Intelligence With Health Policy: A Narrative Review of the Literature

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Assembled Bias: Beyond Transparent Algorithmic Bias

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The Effect of AI-generated Advice on Decision-Making in Personnel SelectionPreprint

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AI and the Philosophy of Expertise and Epistemic Authority

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Integration of Artificial Intelligence and Machine Learning in Business Intelligence

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Beyond Transparency: Computational Reliabilism as an Externalist Epistemology of Algorithms

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What Do Large Language Models Know? Tacit Knowledge as a Potential Causal-Explanatory Structure

Budding, C. (2025) · Philosophy of Science · doi:10.1017/psa.2025.19 · Cited by 7

Application of explainable artificial intelligence to decode water-induced soil erosion in Lidder watershed of the Greater Himalayas

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Counterfactual explanation of Bayesian model uncertainty

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Sources of Understanding in Supervised Machine Learning Models

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Varieties of transparency: exploring agency within AI systems

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Algorithmen und Autonomie

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The Combined Use of GIS and Generative Artificial Intelligence in Detecting Potential Geodiversity Sites and Promoting Geoheritage

Wolniewicz, P. (2024) · Resources · doi:10.3390/resources13090119 · Cited by 9

Using Explainable Artificial Intelligence to Increase Trust in Computer Vision.Preprint

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Revolutionizing flood forecasting by integrating rainfall-runoff correlation analysis with advanced deep learning techniques

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Machine learning and theory-ladenness: a phenomenological account

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Can large language models help solve the cost problem for the right to explanation?

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Incorporating Commonsense Knowledge to Enhance Robot Perception

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How to Make AlphaGo’s Children Explainable

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Identification of Pediatric Bacterial Gastroenteritis From Blood Counts and Interviews Based on Machine Learning

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Does the no miracles argument apply to AI?

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Designing for Complementarity: A Conceptual Framework to Go Beyond the Current Paradigm of Using XAI in Healthcare

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Explainability Is in the Mind of the Beholder: Establishing the Foundations of Explainable Artificial IntelligencePreprint

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EPISTEMOLOGICAL QUESTIONS IN EXPLAINABLE ARTIFICIAL INTELLIGENCE

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Explainable AI for non-experts: Is this a chimera?

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Challenges and future work directions in artificial intelligence with human-computer interaction

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