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Interpretable Decision Sets

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/9781118445112.stat03141openalex-snapshot

Generalized Additive Models

inferredcites →https://doi.org/10.1007/978-3-540-27819-1_16openalex-snapshot

Toward Attribute Efficient Learning of Decision Lists and Parities

inferredcites →https://doi.org/10.1007/978-3-540-73599-1_12openalex-snapshot

Contrast Set Mining for Distinguishing Between Similar Diseases

inferredcites →https://doi.org/10.1007/978-3-642-13657-3_18openalex-snapshot

A New Emerging Pattern Mining Algorithm and Its Application in Supervised Classification

inferredcites →https://doi.org/10.1007/bf02289025openalex-snapshot

An Analytical Solution for Approximating Simple Structure in Factor Analysis

inferredcites →https://doi.org/10.1007/bfb0017011openalex-snapshot

Rule induction with CN2: Some recent improvements

inferredcites →https://doi.org/10.1007/bfb0029561openalex-snapshot

Online Algorithms

inferredcites →https://doi.org/10.1007/bfb0029575openalex-snapshot

On-Line Algorithms in Machine Learning

inferredcites →https://doi.org/10.1007/s10115-008-0136-4openalex-snapshot

One in a million: picking the right patterns

inferredcites →https://doi.org/10.1007/s10115-010-0356-2openalex-snapshot

An overview on subgroup discovery: foundations and applications

inferredcites →https://doi.org/10.1007/s10994-015-5528-6openalex-snapshot

Supersparse linear integer models for optimized medical scoring systems

inferredcites →https://doi.org/10.1016/j.ins.2011.01.039openalex-snapshot

Searching for rules to detect defective modules: A subgroup discovery approach

inferredcites →https://doi.org/10.1016/s0020-0190(99)00031-9openalex-snapshot

The budgeted maximum coverage problem

inferredcites →https://doi.org/10.1016/s0933-3657(98)00070-0openalex-snapshot

Obtaining interpretable fuzzy classification rules from medical data

inferredcites →https://doi.org/10.1023/a:1007678005361openalex-snapshot

Projection Learning

inferredcites →https://doi.org/10.1023/a:1010920819831openalex-snapshot

A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems

inferredcites →https://doi.org/10.1023/a:1022607331053openalex-snapshot

Learning Decision Lists

inferredcites →https://doi.org/10.1023/a:1022641700528openalex-snapshot

The CN2 Induction Algorithm

inferredcites →https://doi.org/10.1037/0033-295x.101.2.343openalex-snapshot

The magical number seven, plus or minus two: Some limits on our capacity for processing information.

inferredcites →https://doi.org/10.1080/15377938.2014.984045openalex-snapshot

The Effect of Race/Ethnicity on Sentencing: Examining Sentence Type, Jail Length, and Prison Length

inferredcites →https://doi.org/10.1109/32.256851openalex-snapshot

Developing interpretable models with optimized set reduction for identifying high-risk software components

inferredcites →https://doi.org/10.1109/91.928739openalex-snapshot

Designing fuzzy inference systems from data: An interpretability-oriented review

inferredcites →https://doi.org/10.1109/icde.2007.367917openalex-snapshot

Discriminative Frequent Pattern Analysis for Effective Classification

inferredcites →https://doi.org/10.1109/icdm.2007.85openalex-snapshot

The Chosen Few: On Identifying Valuable Patterns

inferredcites →https://doi.org/10.1111/j.2041-210x.2010.00012.xopenalex-snapshot

Simple means to improve the interpretability of regression coefficients

inferredcites →https://doi.org/10.1111/j.2517-6161.1996.tb02080.xopenalex-snapshot

Regression Shrinkage and Selection Via the Lasso

inferredcites →https://doi.org/10.1137/090779346openalex-snapshot

Maximizing Non-monotone Submodular Functions

inferredcites →https://doi.org/10.1137/1.9781611972733.40openalex-snapshot

CPAR: Classification based on Predictive Association Rules

inferredcites →https://doi.org/10.1137/1.9781611972757.19openalex-snapshot

HARMONY: Efficiently Mining the Best Rules for Classification

inferredcites →https://doi.org/10.1145/1367497.1367524openalex-snapshot

Optimal marketing strategies over social networks

inferredcites →https://doi.org/10.1145/170035.170072openalex-snapshot

Mining association rules between sets of items in large databases

inferredcites →https://doi.org/10.1145/2339530.2339556openalex-snapshot

Intelligible models for classification and regression

inferredcites →https://doi.org/10.1145/2487575.2487579openalex-snapshot

Accurate intelligible models with pairwise interactions

inferredcites →https://doi.org/10.1145/2783258.2788620openalex-snapshot

A Machine Learning Framework to Identify Students at Risk of Adverse Academic Outcomes

inferredcites →https://doi.org/10.1145/312129.312191openalex-snapshot

Efficient mining of emerging patterns

inferredcites →https://doi.org/10.1145/312129.312263openalex-snapshot

Detecting change in categorical data

inferredcites →https://doi.org/10.1201/9781420010404openalex-snapshot

Generalized Additive Models

inferredcites →https://doi.org/10.1207/s15327906mbr1404_2openalex-snapshot

Very Simple Structure: An Alternative Procedure For Estimating The Optimal Number Of Interpretable Factors

inferredcites →https://doi.org/10.1214/15-aoas848openalex-snapshot

Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model

inferredcites →https://doi.org/10.2307/2290538openalex-snapshot

Generalized Additive Models.

inferredcites →https://doi.org/10.2307/2530946openalex-snapshot

Classification and Regression Trees.

inferredcites →https://doi.org/10.2307/2532174openalex-snapshot

Generalized Additive Models.

inferredcites →https://doi.org/10.3233/ida-2010-0444openalex-snapshot

Rules for contrast sets

inferredcites →https://doi.org/10.3389/fpsyg.2015.00192openalex-snapshot

Bayesian reasoning with ifs and ands and ors

inferredcites →https://doi.org/10.48550/arxiv.1504.07614openalex-snapshot

Or's of And's for Interpretable Classification, with Application to Context-Aware Recommender Systems

inferredcites →https://doi.org/10.48550/arxiv.1606.05798openalex-snapshot

Interpretable Two-level Boolean Rule Learning for Classification

inferredcites →https://doi.org/10.5281/zenodo.1336439openalex-snapshot

Review And Comparison Of Associative Classification Data Mining Approaches

inferredcites →https://doi.org/10.5555/1005332.1005338openalex-snapshot

Subgroup Discovery with CN2-SD

inferredcites →https://doi.org/10.5555/1577069.1577083openalex-snapshot

Supervised Descriptive Rule Discovery: A Unifying Survey of Contrast Set, Emerging Pattern and Subgroup Mining

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.2919024openalex-extracted

Simple Rules for Complex Decisions

inferred← citeshttps://doi.org/10.2139/ssrn.3652970openalex-extracted

inferred← citeshttps://doi.org/10.2139/ssrn.4150381openalex-extracted

inferred← citeshttps://doi.org/10.2139/ssrn.4200133openalex-extracted

Augmenting Human Decision-Making Through a Hierarchical Co-Attention Mechanism: The Case of Art Psychotherapy

inferred← citeshttps://doi.org/10.2139/ssrn.4715286openalex-extracted

Explainable Artificial Intelligence: A Survey of the Need, Techniques, Applications, and Future Direction

inferred← citeshttps://doi.org/10.2139/ssrn.5149144openalex-extracted

Mqg4ai: Towards Responsible High-Risk Ai – Illustrated for Transparency Focusing on Explainability Techniques

inferred← citeshttps://doi.org/10.1002/9781394249312.ch23openalex-snapshot

Explainability and Regulatory Compliance in Healthcare

inferred← citeshttps://doi.org/10.1002/aaai.70043openalex-snapshot

Human at the Center: A Framework for Human‐Driven AI Development

inferred← citeshttps://doi.org/10.1002/ail2.41openalex-snapshot

Explainable, interactive c ontent‐based image retrieval

inferred← citeshttps://doi.org/10.1002/for.70060openalex-snapshot

Validating Explainer Methods: A Functionally Grounded Approach for Numerical Forecasting

inferred← citeshttps://doi.org/10.1002/int.21925openalex-snapshot

Additive Fuzzy Systems: From Generalized Mixtures to Rule Continua

inferred← citeshttps://doi.org/10.1002/sdr.1706openalex-snapshot

Pattern‐oriented analysis of system dynamics models via random forests

inferred← citeshttps://doi.org/10.1002/sta4.633openalex-snapshot

A confidence machine for sparse high‐order interaction model

inferred← citeshttps://doi.org/10.1002/widm.1356openalex-snapshot

Bias in data‐driven artificial intelligence systems—An introductory survey

inferred← citeshttps://doi.org/10.1002/wsbm.1489openalex-snapshot

Molecular networks in Network Medicine: Development and applications

inferred← citeshttps://doi.org/10.1007/978-3-030-17277-0_4openalex-snapshot

Can N-Version Decision-Making Prevent the Rebirth of HAL 9000 in Military Camo? Using a “Golden Rule” Threshold to Prevent AI Mission Individuation

inferred← citeshttps://doi.org/10.1007/978-3-030-17985-4_8openalex-snapshot

Interpretable Machine Learning from Granular Computing Perspective

inferred← citeshttps://doi.org/10.1007/978-3-030-19651-6_42openalex-snapshot

Propositional Rules Generated at the Top Layers of a CNN

inferred← citeshttps://doi.org/10.1007/978-3-030-22868-2_90openalex-snapshot

Explainable Artificial Intelligence Applications in NLP, Biomedical, and Malware Classification: A Literature Review

inferred← citeshttps://doi.org/10.1007/978-3-030-28730-6_4openalex-snapshot

ILIME: Local and Global Interpretable Model-Agnostic Explainer of Black-Box Decision

inferred← citeshttps://doi.org/10.1007/978-3-030-28954-6_2openalex-snapshot

Transparency: Motivations and Challenges

inferred← citeshttps://doi.org/10.1007/978-3-030-30244-3_39openalex-snapshot

Visual Interpretation of Regression Error

inferred← citeshttps://doi.org/10.1007/978-3-030-30391-4_6openalex-snapshot

Explanations of Black-Box Model Predictions by Contextual Importance and Utility

inferred← citeshttps://doi.org/10.1007/978-3-030-30487-4_19openalex-snapshot

Model-Agnostic Explanations for Decisions Using Minimal Patterns

inferred← citeshttps://doi.org/10.1007/978-3-030-32236-6_58openalex-snapshot

A Modified LIME and Its Application to Explain Service Supply Chain Forecasting

inferred← citeshttps://doi.org/10.1007/978-3-030-33607-3_49openalex-snapshot

ALIME: Autoencoder Based Approach for Local Interpretability

inferred← citeshttps://doi.org/10.1007/978-3-030-33778-0_27openalex-snapshot

Sparse Robust Regression for Explaining Classifiers

inferred← citeshttps://doi.org/10.1007/978-3-030-33778-0_28openalex-snapshot

Efficient Discovery of Expressive Multi-label Rules Using Relaxed Pruning

inferred← citeshttps://doi.org/10.1007/978-3-030-33778-0_9openalex-snapshot

On the Trade-Off Between Consistency and Coverage in Multi-label Rule Learning Heuristics

inferred← citeshttps://doi.org/10.1007/978-3-030-43823-4_14openalex-snapshot

Global Explanations with Local Scoring

inferred← citeshttps://doi.org/10.1007/978-3-030-51999-5_5openalex-snapshot

Evaluating Interpretability in Machine Teaching

inferred← citeshttps://doi.org/10.1007/978-3-030-57321-8_24openalex-snapshot

Calibrating Human-AI Collaboration: Impact of Risk, Ambiguity and Transparency on Algorithmic Bias

inferred← citeshttps://doi.org/10.1007/978-3-030-57321-8_3openalex-snapshot

Back to the Feature: A Neural-Symbolic Perspective on Explainable AI

inferred← citeshttps://doi.org/10.1007/978-3-030-58475-7_49openalex-snapshot

Towards Formal Fairness in Machine Learning

inferred← citeshttps://doi.org/10.1007/978-3-030-58475-7_55openalex-snapshot

Computing Optimal Decision Sets with SAT

inferred← citeshttps://doi.org/10.1007/978-3-030-61045-6_10openalex-snapshot

MIRAI: A Modifiable, Interpretable, and Rational AI Decision Support System

inferred← citeshttps://doi.org/10.1007/978-3-030-61527-7_34openalex-snapshot

Multi-directional Rule Set Learning

inferred← citeshttps://doi.org/10.1007/978-3-030-63823-8_44openalex-snapshot

Interpretability of Black Box Models Through Data-View Extraction and Shadow Model Creation

inferred← citeshttps://doi.org/10.1007/978-3-030-76409-8_2openalex-snapshot

Principles of Explainable Artificial Intelligence

inferred← citeshttps://doi.org/10.1007/978-3-030-79463-7_22openalex-snapshot

A Case-Based Reasoning Approach for a Decision Support System in Manufacturing

inferred← citeshttps://doi.org/10.1007/978-3-030-80223-3_18openalex-snapshot

SAT-Based Rigorous Explanations for Decision Lists

inferred← citeshttps://doi.org/10.1007/978-3-030-84060-0_21openalex-snapshot

MAIRE - A Model-Agnostic Interpretable Rule Extraction Procedure for Explaining Classifiers

inferred← citeshttps://doi.org/10.1007/978-3-030-85443-0_6openalex-snapshot

Privacy in Ubiquitous Mobile Computing

inferred← citeshttps://doi.org/10.1007/978-3-030-89811-3_10openalex-snapshot

A Conceptual View on the Design and Properties of Explainable AI Systems for Legal Settings

inferred← citeshttps://doi.org/10.1007/978-3-030-90019-9_3openalex-snapshot

Automatic Generation of Malware Threat Intelligence from Unstructured Malware Traces

inferred← citeshttps://doi.org/10.1007/978-3-030-92681-6_9openalex-snapshot

A Linear-Time Streaming Algorithm for Cardinality-Constrained Maximizing Monotone Non-submodular Set Functions

inferred← citeshttps://doi.org/10.1007/978-3-030-97929-4_3openalex-snapshot

Decrypting the Black Boxing of Artificial Intelligence Using Explainable Artificial Intelligence in Smart Healthcare

inferred← citeshttps://doi.org/10.1007/978-3-030-99772-4_2openalex-snapshot

Adversarial Deep Learning

inferred← citeshttps://doi.org/10.1007/978-3-031-05457-0_21openalex-snapshot

A Review of Framework for Machine Learning Interpretability

inferred← citeshttps://doi.org/10.1007/978-3-031-08333-4_36openalex-snapshot

A Novel Human-Centred Evaluation Approach and an Argument-Based Method for Explainable Artificial Intelligence

inferred← citeshttps://doi.org/10.1007/978-3-031-08757-8_11openalex-snapshot

Neural Additive Models for Explainable Heart Attack Prediction

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The Statistics of Interpretable Machine Learning

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Interpretability and Explainability in Machine Learning

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Integration of Local and Global Features Explanation with Global Rules Extraction and Generation Tools

inferred← citeshttps://doi.org/10.1007/978-3-031-19775-8_17openalex-snapshot

HIVE: Evaluating the Human Interpretability of Visual Explanations

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Explainable for Trustworthy AI

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Truly Unordered Probabilistic Rule Sets for Multi-class Classification

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Discovering Diverse Top-K Characteristic Lists

inferred← citeshttps://doi.org/10.1007/978-3-031-30047-9_27openalex-snapshot

Discovering Rule Lists with Preferred Variables

inferred← citeshttps://doi.org/10.1007/978-3-031-31414-8_2openalex-snapshot

Logic-Based Explainability in Machine Learning

inferred← citeshttps://doi.org/10.1007/978-3-031-35969-9_3openalex-snapshot

Stakeholder-in-the-Loop Fair Decisions: A Framework to Design Decision Support Systems in Public and Private Organizations

inferred← citeshttps://doi.org/10.1007/978-3-031-37731-0_20openalex-snapshot

Comparing Feature Importance and Rule Extraction for Interpretability on Text Data

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CGXplain: Rule-Based Deep Neural Network Explanations Using Dual Linear Programs

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Explaining Deep Learning Based Security Applications

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Learning Locally Interpretable Rule Ensemble

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Contrastive Explanations for Answer-Set Programs

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An Incremental MaxSAT-Based Model to Learn Interpretable and Balanced Classification Rules

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Evaluation of Human-Understandability of Global Model Explanations Using Decision Tree

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DiffVersify: a Scalable Approach to Differentiable Pattern Mining with Coverage Regularization

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Exploring Task-Specific Dimensions in Word Embeddings Through Automatic Rule Learning

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Securing the Future: Addressing Risks and Vulnerabilities in Generative AI for Fintech

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Learning Deep Rule Concepts as Alternating Boolean Pattern Trees

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Estimating Completeness of Consensus Models: Geometrical and Distributional Approaches

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Axe the X in XAI: A Plea for Understandable AI

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On Trustworthy Rule-Based Models and Explanations

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CFIRE: A General Method for Combining Local Explanations

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Evaluating Argumentation Graphs as Global Explainable Surrogate Models for Dense Neural Networks and Their Comparison with Decision Trees

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Rule Extraction and Interaction-Aware Explainability for AI-Driven Malware Detection

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Evaluating Explainability: Metrics, Benchmarks, and Human-Centered Evaluation Methods

inferred← citeshttps://doi.org/10.1007/978-3-032-19105-2_39openalex-snapshot

Rule vs. SHAP: Complementary Tools for Understanding and Verifying ML Models

inferred← citeshttps://doi.org/10.1007/978-3-319-93031-2_3openalex-snapshot

Designing Fair, Efficient, and Interpretable Policies for Prioritizing Homeless Youth for Housing Resources

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A SAT-Based Approach to Learn Explainable Decision Sets

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Computing with SAT Oracles: Past, Present and Future

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Considerations for Evaluation and Generalization in Interpretable Machine Learning

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Explaining Black-Box Classifiers with ILP – Empowering LIME with Aleph to Approximate Non-linear Decisions with Relational Rules

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Classification and Prediction of Text Data by Using a Natural Language Processing Algorithm

inferred← citeshttps://doi.org/10.1007/978-981-16-9447-9_47openalex-snapshot

Explainability of Deep Learning-Based System in Health Care

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Explainable AI and Trust in Healthcare: Bridging the Gap Between Technology and Clinical Practice

inferred← citeshttps://doi.org/10.1007/978-981-95-3811-9_2openalex-snapshot

The Importance of Explainable Artificial Intelligence for Informed Consent

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Enhancing Trust in Healthcare Diagnosis Through Explainable AI

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Symbolic Data Analysis to Improve Completeness of Model Combination Methods

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An explanation space to align user studies with the technical development of Explainable AI

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Analyzing feature importance with neural-network-derived trees

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Best of both worlds: contextual feature explanation

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Personalized interpretable classification

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Enhancing the prediction for shunt-dependent hydrocephalus after aneurysmal subarachnoid hemorrhage using a machine learning approach

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The efficacy of machine learning algorithms in evaluating factors associated with shunt-dependent hydrocephalus after subarachnoid hemorrhage: a systematic review and meta-analysis

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CHIRPS: Explaining random forest classification

inferred← citeshttps://doi.org/10.1007/s10462-022-10256-8openalex-snapshot

A global taxonomy of interpretable AI: unifying the terminology for the technical and social sciences

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Developing user-centered system design guidelines for explainable AI: a systematic literature review

inferred← citeshttps://doi.org/10.1007/s10489-021-02595-wopenalex-snapshot

MoMAC: Multi-objective optimization to combine multiple association rules into an interpretable classification

inferred← citeshttps://doi.org/10.1007/s10489-022-04370-xopenalex-snapshot

QCBA: improving rule classifiers learned from quantitative data by recovering information lost by discretisation

inferred← citeshttps://doi.org/10.1007/s10489-024-05509-8openalex-snapshot

A survey of explainable knowledge tracing

inferred← citeshttps://doi.org/10.1007/s10515-022-00338-wopenalex-snapshot

Which neural network makes more explainable decisions? An approach towards measuring explainability

inferred← citeshttps://doi.org/10.1007/s10618-022-00878-5openalex-snapshot

Stable and actionable explanations of black-box models through factual and counterfactual rules

inferred← citeshttps://doi.org/10.1007/s10618-023-00933-9openalex-snapshot

Benchmarking and survey of explanation methods for black box models

inferred← citeshttps://doi.org/10.1007/s10654-025-01215-yopenalex-snapshot

Two-step pragmatic subgroup discovery for heterogeneous treatment effects analyses: perspectives toward enhanced interpretability

inferred← citeshttps://doi.org/10.1007/s10703-023-00410-5openalex-snapshot

Functional synthesis via input–output separation

inferred← citeshttps://doi.org/10.1007/s10796-025-10683-2openalex-snapshot

Fuzzy Decision Trees for Explainable Brain Tumor Classification: A Comparative Study with Deep Neural Networks and Classical Binary Decision Trees

inferred← citeshttps://doi.org/10.1007/s10796-026-10697-4openalex-snapshot

Human-Centered Explainable AI for Time Series Forecasting: A Case Study in the Pulp and Paper Industry

inferred← citeshttps://doi.org/10.1007/s10817-018-9499-8openalex-snapshot

Explaining AI Decisions Using Efficient Methods for Learning Sparse Boolean Formulae

inferred← citeshttps://doi.org/10.1007/s10994-018-5707-3openalex-snapshot

Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP

inferred← citeshttps://doi.org/10.1007/s10994-020-05901-8openalex-snapshot

A decision-theoretic approach for model interpretability in Bayesian framework

inferred← citeshttps://doi.org/10.1007/s10994-021-06048-wopenalex-snapshot

Generating contrastive explanations for inductive logic programming based on a near miss approach

inferred← citeshttps://doi.org/10.1007/s10994-022-06278-6openalex-snapshot

FFNSL: Feed-Forward Neural-Symbolic Learner

inferred← citeshttps://doi.org/10.1007/s10994-022-06290-wopenalex-snapshot

Efficient learning of large sets of locally optimal classification rules

inferred← citeshttps://doi.org/10.1007/s10994-024-06643-7openalex-snapshot

Interpretable optimisation-based approach for hyper-box classification

inferred← citeshttps://doi.org/10.1007/s11023-024-09700-1openalex-snapshot

Understanding with Toy Surrogate Models in Machine Learning

inferred← citeshttps://doi.org/10.1007/s11042-024-18352-3openalex-snapshot

Heterogeneous transfer learning: recent developments, applications, and challenges

inferred← citeshttps://doi.org/10.1007/s11063-021-10664-xopenalex-snapshot

Classification Algorithm Using Branches Importance

inferred← citeshttps://doi.org/10.1007/s11634-020-00419-2openalex-snapshot

Editable machine learning models? A rule-based framework for user studies of explainability

inferred← citeshttps://doi.org/10.1007/s11634-023-00553-7openalex-snapshot

On the efficient implementation of classification rule learning

inferred← citeshttps://doi.org/10.1007/s12597-020-00502-4openalex-snapshot

Predictive asset availability optimization for underground trucks and loaders in the mining industry

inferred← citeshttps://doi.org/10.1007/s12650-019-00607-zopenalex-snapshot

Visualizing surrogate decision trees of convolutional neural networks

inferred← citeshttps://doi.org/10.1007/s13218-018-0565-5openalex-snapshot

Please delete that! Why should I?

inferred← citeshttps://doi.org/10.1007/s13218-020-00633-2openalex-snapshot

Mutual Explanations for Cooperative Decision Making in Medicine

inferred← citeshttps://doi.org/10.1007/s13218-022-00786-2openalex-snapshot

What is Missing in XAI So Far?

inferred← citeshttps://doi.org/10.1007/s13278-024-01389-5openalex-snapshot

Deepthreatexplainer: a united explainable predictor for threat comments identification on Twitter

inferred← citeshttps://doi.org/10.1007/s13347-022-00524-4openalex-snapshot

Sources of Understanding in Supervised Machine Learning Models

inferred← citeshttps://doi.org/10.1007/s13755-024-00303-9openalex-snapshot

Explainable depression symptom detection in social media

inferred← citeshttps://doi.org/10.1007/s41060-023-00398-5openalex-snapshot

Re-interpreting rules interpretability

inferred← citeshttps://doi.org/10.1007/s41060-023-00445-1openalex-snapshot

Through the looking glass: evaluating post hoc explanations using transparent models

inferred← citeshttps://doi.org/10.1007/s44443-026-00535-7openalex-snapshot

Removing $$\delta $$-dependence in minimal interpretable model learning: distribution conditions and structural parameters

inferred← citeshttps://doi.org/10.1007/s44443-026-00833-0openalex-snapshot

ASP-KNN: a declarative, constraint-aware, and explainable reformulation of k-Nearest Neighbors in answer set programming

inferred← citeshttps://doi.org/10.1016/j.array.2026.101267openalex-snapshot

An explainable multimodal data fusion framework for compatibility reasoning in heterogeneous decision systems

inferred← citeshttps://doi.org/10.1016/j.artint.2020.103434openalex-snapshot

Spatial relation learning for explainable image classification and annotation in critical applications

inferred← citeshttps://doi.org/10.1016/j.artint.2021.103457openalex-snapshot

GLocalX - From Local to Global Explanations of Black Box AI Models

inferred← citeshttps://doi.org/10.1016/j.artint.2021.103458openalex-snapshot

A review of possible effects of cognitive biases on interpretation of rule-based machine learning models

inferred← citeshttps://doi.org/10.1016/j.artint.2021.103507openalex-snapshot

“That's (not) the output I expected!” On the role of end user expectations in creating explanations of AI systems

inferred← citeshttps://doi.org/10.1016/j.bspc.2025.108153openalex-snapshot

Interpretability and accuracy of machine learning algorithms for biomedical time series analysis – a scoping review

inferred← citeshttps://doi.org/10.1016/j.cag.2019.08.012openalex-snapshot

Visual exploration of an ensemble of classifiers

inferred← citeshttps://doi.org/10.1016/j.cell.2018.05.015openalex-snapshot

Next-Generation Machine Learning for Biological Networks

inferred← citeshttps://doi.org/10.1016/j.clsr.2019.01.003openalex-snapshot

Profiling tax and financial behaviour with big data under the GDPR

inferred← citeshttps://doi.org/10.1016/j.compbiomed.2022.105587openalex-snapshot

The effect of machine learning explanations on user trust for automated diagnosis of COVID-19

inferred← citeshttps://doi.org/10.1016/j.cor.2025.107283openalex-snapshot

A unified approach to extract interpretable rules from tree ensembles via Integer Programming

inferred← citeshttps://doi.org/10.1016/j.cose.2024.104211openalex-snapshot

An on-the-fly framework for usable access control policy mining

inferred← citeshttps://doi.org/10.1016/j.dib.2022.108382openalex-snapshot

Flow cytometry datasets consisting of peripheral blood and bone marrow samples for the evaluation of explainable artificial intelligence methods

inferred← citeshttps://doi.org/10.1016/j.eml.2022.101895openalex-snapshot

How to see hidden patterns in metamaterials with interpretable machine learning

inferred← citeshttps://doi.org/10.1016/j.eswa.2018.09.056openalex-snapshot

Specifics of medical data mining for diagnosis aid: A survey

inferred← citeshttps://doi.org/10.1016/j.eswa.2020.113941openalex-snapshot

Post-hoc explanation of black-box classifiers using confident itemsets

inferred← citeshttps://doi.org/10.1016/j.eswa.2021.115045openalex-snapshot

CASTLE: Cluster-aided space transformation for local explanations

inferred← citeshttps://doi.org/10.1016/j.eswa.2023.121365openalex-snapshot

Concise rule induction algorithm based on one-sided maximum decision tree approach

inferred← citeshttps://doi.org/10.1016/j.future.2026.108807openalex-snapshot

MLOps for enterprise AI: A systematic literature review of frameworks, interpretability practices, and organisational readiness

inferred← citeshttps://doi.org/10.1016/j.ijar.2024.109206openalex-snapshot

Synergies between machine learning and reasoning - An introduction by the Kay R. Amel group

inferred← citeshttps://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

inferred← citeshttps://doi.org/10.1016/j.inffus.2024.102472openalex-snapshot

Explainable AI-driven IoMT fusion: Unravelling techniques, opportunities, and challenges with Explainable AI in healthcare

inferred← citeshttps://doi.org/10.1016/j.ins.2019.10.050openalex-snapshot

Interpretable multiclass classification by MDL-based rule lists

inferred← citeshttps://doi.org/10.1016/j.ins.2020.05.126openalex-snapshot

LoRMIkA: Local rule-based model interpretability with k-optimal associations

inferred← citeshttps://doi.org/10.1016/j.is.2020.101549openalex-snapshot

Explaining data with descriptions

inferred← citeshttps://doi.org/10.1016/j.isci.2021.102249openalex-snapshot

Ethical machines: The human-centric use of artificial intelligence

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Recommendations to promote fairness and inclusion in biomedical AI research and clinical use

inferred← citeshttps://doi.org/10.1016/j.jocs.2021.101539openalex-snapshot

Validation of XAI explanations for multivariate time series classification in the maritime domain

inferred← citeshttps://doi.org/10.1016/j.knosys.2025.113092openalex-snapshot

Towards consistency of rule-based explainer and black box model — Fusion of rule induction and XAI-based feature importance

inferred← citeshttps://doi.org/10.1016/j.media.2023.102941openalex-snapshot

Deep learning with explainability for characterizing age-related intrinsic differences in dynamic brain functional connectivity

inferred← citeshttps://doi.org/10.1016/j.neucom.2024.128111openalex-snapshot

Explainable artificial intelligence: A survey of needs, techniques, applications, and future direction

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Evaluation of non-negative matrix factorization of grey matter in age prediction

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Joint multi-label learning and feature extraction for temporal link prediction

inferred← citeshttps://doi.org/10.1016/j.patrec.2021.05.018openalex-snapshot

PASTLE: Pivot-aided space transformation for local explanations

inferred← citeshttps://doi.org/10.1016/j.patter.2023.100780openalex-snapshot

Perspectives on incorporating expert feedback into model updates

inferred← citeshttps://doi.org/10.1016/j.physrep.2019.12.004openalex-snapshot

Computational network biology: Data, models, and applications

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Towards Explainable CNNs for Android Malware Detection

inferred← citeshttps://doi.org/10.1016/j.tcs.2025.115344openalex-snapshot

Decision trees with short explainable rules

inferred← citeshttps://doi.org/10.1017/9781108872225.010openalex-snapshot

Humans and Machine Learning

inferred← citeshttps://doi.org/10.1017/9781108872225.011openalex-snapshot

Human-Centered Explainable AI (XAI)

inferred← citeshttps://doi.org/10.1017/dap.2023.2openalex-snapshot

Explainable machine learning for public policy: Use cases, gaps, and research directions

inferred← citeshttps://doi.org/10.1017/s0003055419000522openalex-snapshot

Understanding Delegation Through Machine Learning: A Method and Application to the European Union

inferred← citeshttps://doi.org/10.1017/s1471068424000401openalex-snapshot

Generating Global and Local Explanations for Tree-Ensemble Learning Methods by Answer Set Programming

inferred← citeshttps://doi.org/10.1038/s41598-022-08863-0openalex-snapshot

Using deep learning to predict human decisions and using cognitive models to explain deep learning models

inferred← citeshttps://doi.org/10.1038/s41598-023-38459-1openalex-snapshot

E pluribus unum interpretable convolutional neural networks

inferred← citeshttps://doi.org/10.1038/s42256-023-00692-8openalex-snapshot

Explaining machine learning models with interactive natural language conversations using TalkToModel

inferred← citeshttps://doi.org/10.1063/5.0323968openalex-snapshot

Improving the efficiency of regression decision trees by applying the differential evolution method

inferred← citeshttps://doi.org/10.1073/pnas.2310151122openalex-snapshot

Fast Interpretable Greedy-Tree Sums

inferred← citeshttps://doi.org/10.1080/03081079.2024.2384404openalex-snapshot

Stacking GA 2 M for inherently interpretable fraudulent reviewer identification by fusing target and non-target features

inferred← citeshttps://doi.org/10.1080/07370024.2020.1734931openalex-snapshot

Interactive machine teaching: a human-centered approach to building machine-learned models

inferred← citeshttps://doi.org/10.1080/10447318.2022.2093773openalex-snapshot

Transparent-AI Blueprint: Developing a Conceptual Tool to Support the Design of Transparent AI Agents

inferred← citeshttps://doi.org/10.1080/10447318.2024.2364986openalex-snapshot

May I Ask a Follow-up Question? Understanding the Benefits of Conversations in Neural Network Explainability

inferred← citeshttps://doi.org/10.1089/nsm.2020.0004openalex-snapshot

Network and Systems Medicine: Position Paper of the European Collaboration on Science and Technology Action on Open Multiscale Systems Medicine

inferred← citeshttps://doi.org/10.1093/bib/bbad149openalex-snapshot

SSNMDI: a novel joint learning model of semi-supervised non-negative matrix factorization and data imputation for clustering of single-cell RNA-seq data

inferred← citeshttps://doi.org/10.1093/ectj/utaa005openalex-snapshot

Artificial intelligence as structural estimation: Deep Blue, Bonanza, and AlphaGo

inferred← citeshttps://doi.org/10.1093/infdis/jiad158openalex-snapshot

Artificial Intelligence and Infectious Disease Imaging

inferred← citeshttps://doi.org/10.1093/jamia/ocaa053openalex-snapshot

Explainable artificial intelligence models using real-world electronic health record data: a systematic scoping review

inferred← citeshttps://doi.org/10.1093/jamia/ocaf058openalex-snapshot

Improving clinical decision support through interpretable machine learning and error handling in electronic health records

inferred← citeshttps://doi.org/10.1093/oxfordhb/9780198857815.013.20openalex-snapshot

Interpretability and Transparency in Artificial Intelligence

inferred← citeshttps://doi.org/10.1101/2019.12.12.874784openalex-snapshot

Interpretable Deep Temporal Structure Learning Model for Early Detection of Alzheimer’s Disease

inferred← citeshttps://doi.org/10.1101/2024.07.28.24311114openalex-snapshot

Comparative Evaluation Of Machine Learning Classifiers For Brain Tumor Detection

inferred← citeshttps://doi.org/10.1109/access.2020.2971576openalex-snapshot

Human Digital Twin for Fitness Management

inferred← citeshttps://doi.org/10.1109/access.2020.2975584openalex-snapshot

Spatial Structured Prediction Models: Applications, Challenges, and Techniques

inferred← citeshttps://doi.org/10.1109/access.2020.2985255openalex-snapshot

A New Splitting Criterion for Better Interpretable Trees

inferred← citeshttps://doi.org/10.1109/access.2020.3008475openalex-snapshot

Using Partial Combination Models to Improve Prediction Quality and Transparency in Mixed Datasets

inferred← citeshttps://doi.org/10.1109/access.2020.3020638openalex-snapshot

Copying Machine Learning Classifiers

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