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Explainable machine learning in deployment

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What this work says

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

inferredcites →https://doi.org/10.1007/978-1-4615-0005-6_16openalex-snapshot

Data Squashing: Constructing Summary Data Sets

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Transparency: Motivations and Challenges

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Explaining prediction models and individual predictions with feature contributions

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Top-Down Neural Attention by Excitation Backprop

inferredcites →https://doi.org/10.1007/s13347-017-0279-xopenalex-snapshot

Fair, Transparent, and Accountable Algorithmic Decision-making Processes

inferredcites →https://doi.org/10.1016/j.artint.2018.07.007openalex-snapshot

Explanation in artificial intelligence: Insights from the social sciences

inferredcites →https://doi.org/10.1016/j.artint.2021.103502openalex-snapshot

Explaining individual predictions when features are dependent: More accurate approximations to Shapley values

inferredcites →https://doi.org/10.1016/j.patcog.2016.11.008openalex-snapshot

Explaining nonlinear classification decisions with deep Taylor decomposition

inferredcites →https://doi.org/10.1017/cbo9780511528446.003openalex-snapshot

A value for n -person games

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Explainable machine-learning predictions for the prevention of hypoxaemia during surgery

inferredcites →https://doi.org/10.1038/s41591-018-0107-6openalex-snapshot

Clinically applicable deep learning for diagnosis and referral in retinal disease

inferredcites →https://doi.org/10.1038/s42256-019-0048-xopenalex-snapshot

Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

inferredcites →https://doi.org/10.1080/01621459.1986.10478354openalex-snapshot

Statistics and Causal Inference

inferredcites →https://doi.org/10.1080/09672559.2018.1454637openalex-snapshot

Linking Trust to Trustworthiness

inferredcites →https://doi.org/10.1109/cvpr.2018.00156openalex-snapshot

Excitation Backprop for RNNs

inferredcites →https://doi.org/10.1109/dsaa.2018.00018openalex-snapshot

Explaining Explanations: An Overview of Interpretability of Machine Learning

inferredcites →https://doi.org/10.1109/iccv.2017.371openalex-snapshot

Interpretable Explanations of Black Boxes by Meaningful Perturbation

inferredcites →https://doi.org/10.1109/tvcg.2019.2934619openalex-snapshot

The What-If Tool: Interactive Probing of Machine Learning Models

inferredcites →https://doi.org/10.1126/science.aal4321openalex-snapshot

Beyond prediction: Using big data for policy problems

inferredcites →https://doi.org/10.1145/2939672.2939778openalex-snapshot

"Why Should I Trust You?"

inferredcites →https://doi.org/10.1145/3173574.3173860openalex-snapshot

Towards Algorithmic Experience

inferredcites →https://doi.org/10.1145/3287560.3287562openalex-snapshot

Model Reconstruction from Model Explanations

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Actionable Recourse in Linear Classification

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Explaining Explanations in AI

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Model Cards for Model Reporting

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Improving Fairness in Machine Learning Systems

inferredcites →https://doi.org/10.1145/3411764.3445315openalex-snapshot

Manipulating and Measuring Model Interpretability

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On the Privacy Risks of Model Explanations

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Prediction Policy Problems

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The State of Applied Econometrics: Causality and Policy Evaluation

inferredcites →https://doi.org/10.1515/9781400881970-018openalex-snapshot

17. A Value for n-Person Games

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Interpretation of Neural Networks Is Fragile

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Interpretable and Differentially Private Predictions

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The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation

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The Intuitive Appeal of Explainable Machines

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Detection of Influential Observation in Linear Regression

inferredcites →https://doi.org/10.2307/3182612openalex-snapshot

CAUSALITY: MODELS, REASONING AND INFERENCE

inferredcites →https://doi.org/10.24963/ijcai.2017/371openalex-snapshot

Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations

inferredcites →https://doi.org/10.3929/ethz-b-000342737openalex-snapshot

Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Values Approximation

inferredcites →https://doi.org/10.4230/lipics.concur.2020.3openalex-snapshot

Agnostic Learning with Unknown Utilities

inferredcites →https://doi.org/10.4230/oasics.dx.2024.27openalex-snapshot

On a Method to Measure Supervised Multiclass Model’s Interpretability: Application to Degradation Diagnosis (Short Paper)

inferredcites →https://doi.org/10.48550/arxiv.0912.1128openalex-snapshot

How to Explain Individual Classification Decisions

inferredcites →https://doi.org/10.48550/arxiv.1602.06561openalex-snapshot

Deep Learning in Finance

inferredcites →https://doi.org/10.48550/arxiv.1702.08608openalex-snapshot

Towards A Rigorous Science of Interpretable Machine Learning

inferredcites →https://doi.org/10.48550/arxiv.1703.01365openalex-snapshot

Axiomatic Attribution for Deep Networks

inferredcites →https://doi.org/10.48550/arxiv.1703.04730openalex-snapshot

Understanding Black-box Predictions via Influence Functions

inferredcites →https://doi.org/10.48550/arxiv.1704.02685openalex-snapshot

Learning Important Features Through Propagating Activation Differences

inferredcites →https://doi.org/10.48550/arxiv.1706.03825openalex-snapshot

SmoothGrad: removing noise by adding noise

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Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)

inferredcites →https://doi.org/10.48550/arxiv.1807.07506openalex-snapshot

Improving Simple Models with Confidence Profiles

inferredcites →https://doi.org/10.48550/arxiv.1810.00184openalex-snapshot

Stakeholders in Explainable AI

inferredcites →https://doi.org/10.48550/arxiv.1810.10752openalex-snapshot

Word Embedding based Edit Distance

inferredcites →https://doi.org/10.48550/arxiv.1901.10040openalex-snapshot

Towards Aggregating Weighted Feature Attributions

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Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation

inferredcites →https://doi.org/10.48550/arxiv.1904.12991openalex-snapshot

"Why Should You Trust My Explanation?" Understanding Uncertainty in LIME Explanations

inferredcites →https://doi.org/10.48550/arxiv.1905.02175openalex-snapshot

Adversarial Examples Are Not Bugs, They Are Features

inferredcites →https://doi.org/10.48550/arxiv.1905.04172openalex-snapshot

On the Connection Between Adversarial Robustness and Saliency Map Interpretability

inferredcites →https://doi.org/10.48550/arxiv.1906.07983openalex-snapshot

Explanations can be manipulated and geometry is to blame

inferredcites →https://doi.org/10.5860/choice.38sup-465openalex-snapshot

Board of Governors of the Federal Reserve System

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

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Epistemic virtues and ethics of explanation in machine learning

inferred← citeshttps://doi.org/10.1007/s44230-024-00072-4openalex-snapshot

Irrigation with Artificial Intelligence: Problems, Premises, Promises

inferred← citeshttps://doi.org/10.1007/s44244-023-00009-zopenalex-snapshot

Approach to provide interpretability in machine learning models for image classification

inferred← citeshttps://doi.org/10.1016/bs.mcps.2026.07.006openalex-snapshot

Ethics in AI-driven safety culture

inferred← citeshttps://doi.org/10.1016/j.accinf.2025.100739openalex-snapshot

Artificial intelligence auditability and auditor readiness for auditing artificial intelligence systems

inferred← citeshttps://doi.org/10.1016/j.artint.2021.103471openalex-snapshot

Using ontologies to enhance human understandability of global post-hoc explanations of black-box models

inferred← citeshttps://doi.org/10.1016/j.artmed.2021.102087openalex-snapshot

Prediction of weaning from mechanical ventilation using Convolutional Neural Networks

inferred← citeshttps://doi.org/10.1016/j.bbi.2023.11.005openalex-snapshot

Machine learning and artificial intelligence in neuroscience: A primer for researchers

inferred← citeshttps://doi.org/10.1016/j.bspc.2024.106136openalex-snapshot

Data-driven time series analysis of sensory cortical processing using high-resolution fMRI across different studies

inferred← citeshttps://doi.org/10.1016/j.clsr.2022.105667openalex-snapshot

Hidden depths: The effects of extrinsic data collection on consumer insurance contracts

inferred← citeshttps://doi.org/10.1016/j.cma.2022.115732openalex-snapshot

Automated learning of interpretable models with quantified uncertainty

inferred← citeshttps://doi.org/10.1016/j.comnet.2022.109466openalex-snapshot

Explainable Artificial Intelligence in communication networks: A use case for failure identification in microwave networks

inferred← citeshttps://doi.org/10.1016/j.comnet.2025.111433openalex-snapshot

Transformers model for DDoS attack detection: A survey

inferred← citeshttps://doi.org/10.1016/j.compag.2025.109990openalex-snapshot

Explainable machine learning models for corn yield prediction using UAV multispectral data

inferred← citeshttps://doi.org/10.1016/j.compositesa.2025.109400openalex-snapshot

Quantitative damage characterization in CFRPs using an interpretable CNN: A CT-based deep learning approach

inferred← citeshttps://doi.org/10.1016/j.dajour.2023.100342openalex-snapshot

An integrated machine learning and MARCOS method for supplier evaluation and selection

inferred← citeshttps://doi.org/10.1016/j.dajour.2025.100547openalex-snapshot

A systematic review of machine learning applications in sustainable supplier selection

inferred← citeshttps://doi.org/10.1016/j.dsm.2022.07.004openalex-snapshot

Monitoring machine learning models: a categorization of challenges and methods

inferred← citeshttps://doi.org/10.1016/j.engappai.2023.106248openalex-snapshot

Uncertainty-aware credit card fraud detection using deep learning

inferred← citeshttps://doi.org/10.1016/j.envres.2026.123853openalex-snapshot

Applications of machine learning-based quantitative structure activity relationship (ML-QSAR) models in environmental science

inferred← citeshttps://doi.org/10.1016/j.eplepsyres.2026.107766openalex-snapshot

Predicting seizure-free outcomes in people with treatment-resistant epilepsy: A machine learning approach

inferred← citeshttps://doi.org/10.1016/j.eswa.2023.121689openalex-snapshot

Code-aware fault localization with pre-training and interpretable machine learning

inferred← citeshttps://doi.org/10.1016/j.eswa.2024.124265openalex-snapshot

A prediction framework for pharmaceutical drug consumption using short time-series

inferred← citeshttps://doi.org/10.1016/j.fuel.2025.135073openalex-snapshot

Explainable artificial intelligence models for estimating the heat capacity of deep eutectic solvents

inferred← citeshttps://doi.org/10.1016/j.future.2020.10.015openalex-snapshot

FLAGS: A methodology for adaptive anomaly detection and root cause analysis on sensor data streams by fusing expert knowledge with machine learning

inferred← citeshttps://doi.org/10.1016/j.future.2026.108649openalex-snapshot

AuditOps: A continuous compliance framework for integrating regulatory requirements in the MLOps lifecycle

inferred← citeshttps://doi.org/10.1016/j.ibmed.2025.100237openalex-snapshot

Development of an explainable machine learning model for predicting neurological deterioration in spontaneous intracerebral hemorrhage

inferred← citeshttps://doi.org/10.1016/j.inffus.2021.11.007openalex-snapshot

Explaining the impact of source behaviour in evidential reasoning

inferred← citeshttps://doi.org/10.1016/j.infsof.2024.107456openalex-snapshot

The need for more informative defect prediction: A systematic literature review

inferred← citeshttps://doi.org/10.1016/j.ipm.2022.103111openalex-snapshot

A survey on XAI and natural language explanations

inferred← citeshttps://doi.org/10.1016/j.jclepro.2023.136605openalex-snapshot

Creation of sustainable growth with explainable artificial intelligence: An empirical insight from consumer packaged goods retailers

inferred← citeshttps://doi.org/10.1016/j.jclinepi.2021.11.001openalex-snapshot

Believing in black boxes: machine learning for healthcare does not need explainability to be evidence-based

inferred← citeshttps://doi.org/10.1016/j.jechem.2023.10.049openalex-snapshot

Machine learning in metal-ion battery research: Advancing material prediction, characterization, and status evaluation

inferred← citeshttps://doi.org/10.1016/j.jisa.2025.103971openalex-snapshot

FRAPE: A Framework for Risk Assessment, Prioritization and Explainability of vulnerabilities in cybersecurity

inferred← citeshttps://doi.org/10.1016/j.jksuci.2023.03.016openalex-snapshot

An ensemble face recognition mechanism based on three-way decisions

inferred← citeshttps://doi.org/10.1016/j.jocm.2021.100340openalex-snapshot

Choice modelling in the age of machine learning - Discussion paper

inferred← citeshttps://doi.org/10.1016/j.jrt.2021.100017openalex-snapshot

Explainability for experts: A design framework for making algorithms supporting expert decisions more explainable

inferred← citeshttps://doi.org/10.1016/j.jss.2025.112457openalex-snapshot

A comparative analysis of ML techniques for bug report classification

inferred← citeshttps://doi.org/10.1016/j.knosys.2023.111107openalex-snapshot

A unified and practical user-centric framework for explainable artificial intelligence

inferred← citeshttps://doi.org/10.1016/j.mlwa.2021.100172openalex-snapshot

Explainable outlier detection: What, for Whom and Why?

inferred← citeshttps://doi.org/10.1016/j.neunet.2024.106896openalex-snapshot

TV-Net: Temporal-Variable feature harmonizing Network for multivariate time series classification and interpretation

inferred← citeshttps://doi.org/10.1016/j.patrec.2021.06.030openalex-snapshot

Perturbation-based methods for explaining deep neural networks: A survey

inferred← citeshttps://doi.org/10.1016/j.procir.2022.05.068openalex-snapshot

Guideline for Deployment of Machine Learning Models for Predictive Quality in Production

inferred← citeshttps://doi.org/10.1016/j.procs.2020.07.074openalex-snapshot

Combining Machine Learning with Visual Analytics for Explainable Forecasting of Energy Demand in Prosumer Scenarios

inferred← citeshttps://doi.org/10.1016/j.psep.2022.12.018openalex-snapshot

Review of interpretable machine learning for process industries

inferred← citeshttps://doi.org/10.1016/j.trd.2022.103361openalex-snapshot

Driving the environmental extra mile – Car sharing and voluntary carbon dioxide offsetting

inferred← citeshttps://doi.org/10.1017/9781108939010.035openalex-snapshot

Applications

inferred← citeshttps://doi.org/10.1017/9781108939010.043openalex-snapshot

Natural Language Processing in Behavioural Data Science

inferred← citeshttps://doi.org/10.1017/dap.2021.37openalex-snapshot

From transparency to accountability of intelligent systems: Moving beyond aspirations

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/dap.2023.8openalex-snapshot

Think about the stakeholders first! Toward an algorithmic transparency playbook for regulatory compliance

inferred← citeshttps://doi.org/10.1017/dap.2024.48openalex-snapshot

A Machine Learning architecture to forecast Irregular Border Crossings and Asylum requests for policy support in Europe: a case study

inferred← citeshttps://doi.org/10.1021/acsnano.5c21738openalex-snapshot

Interpretable Machine Learning for Solid-State Batteries

inferred← citeshttps://doi.org/10.1029/2021wr031808openalex-snapshot

Machine Learning‐Based Surrogate Modeling for Urban Water Networks: Review and Future Research Directions

inferred← citeshttps://doi.org/10.1029/2025wr041178openalex-snapshot

Comparing Snow Water Equivalent Estimations From Long Short‐Term Memory Networks and Physics‐Based Models in the Western United States

inferred← citeshttps://doi.org/10.1029/2026ef008857openalex-snapshot

Reimagining How Flood Warnings Can Inform Decision‐Making and Community Actions

inferred← citeshttps://doi.org/10.1038/s41467-022-33128-9openalex-snapshot

Technology readiness levels for machine learning systems

inferred← citeshttps://doi.org/10.1038/s41598-022-18366-7openalex-snapshot

Rapidly predicting Kohn–Sham total energy using data-centric AI

inferred← citeshttps://doi.org/10.1038/s41598-025-17703-wopenalex-snapshot

Explainability and importance estimate of time series classifier via embedded neural network

inferred← citeshttps://doi.org/10.1038/s44159-025-00499-xopenalex-snapshot

A cognitive approach to human–AI complementarity in dynamic decision-making

inferred← citeshttps://doi.org/10.1049/pbse014e_ch5openalex-snapshot

Towards an accountable Internet of Things: a call for reviewability

inferred← citeshttps://doi.org/10.1061/jccee5.cpeng-6209openalex-snapshot

Causality-Guided Explainable Deep-Learning Model for Managing Tunnel-Induced Ground Settlement Risks

inferred← citeshttps://doi.org/10.1061/jsendh.steng-15222openalex-snapshot

Making the Black Box Transparent: State of the Art in Explainable Machine Learning for Structural Design and Assessment

inferred← citeshttps://doi.org/10.1073/pnas.2318124121openalex-snapshot

Evaluating language models for mathematics through interactions

inferred← citeshttps://doi.org/10.1080/02564602.2025.2512086openalex-snapshot

Evolution of AI-Driven Decision Making with Decision Support Systems, Expert Systems, Recommender Systems, and XAI

inferred← citeshttps://doi.org/10.1080/02642069.2026.2613654openalex-snapshot

Strategic and ethical enablers of responsible AI adoption in service industries: an exploratory causal Fuzzy Delphi – DEMATEL approach

inferred← citeshttps://doi.org/10.1080/07370024.2024.2307610openalex-snapshot

Technology acceptance and transparency demands for toxic language classification – interviews with moderators of public online discussion fora

inferred← citeshttps://doi.org/10.1080/10408347.2026.2694610openalex-snapshot

From Data to Decision: Standardization, Validation, and Regulatory Readiness of AI-Driven Analytical Chemistry Workflows

inferred← citeshttps://doi.org/10.1080/10447318.2025.2606953openalex-snapshot

Trust Me, I’m Transparent: Describing AI Systems Using Global Explanations

inferred← citeshttps://doi.org/10.1080/10447318.2026.2638556openalex-snapshot

Evaluating Explanation Strategies in Patient-Facing Diagnostic AI: A Comparative Study of Decision Tree, Example-Based, and Feature Attribution Approaches

inferred← citeshttps://doi.org/10.1080/12460125.2022.2119707openalex-snapshot

User-centric explainable AI: design and evaluation of an approach to generate coherent counterfactual explanations for structured data

inferred← citeshttps://doi.org/10.1080/13467581.2023.2278460openalex-snapshot

Sequential attention deep learning architecture with unsupervised pre-training for interpretable and accurate building energy prediction with limited data

inferred← citeshttps://doi.org/10.1080/13600869.2022.2060469openalex-snapshot

There and back again: how target market determination obligations for financial products may incentivise consumer data profiling

inferred← citeshttps://doi.org/10.1080/1369118x.2024.2400130openalex-snapshot

Explaining machine learning practice: findings from an engaged science and technology studies project

inferred← citeshttps://doi.org/10.1088/1361-6579/acb24dopenalex-snapshot

Automated detection of schizophrenia using deep learning: a review for the last decade

inferred← citeshttps://doi.org/10.1093/9780197851418.003.0994openalex-snapshot

Explainability and Interpretability in Machine Learning and Artificial Intelligence

inferred← citeshttps://doi.org/10.1093/bib/bbad236openalex-snapshot

Explainable AI for Bioinformatics: Methods, Tools and Applications

inferred← citeshttps://doi.org/10.1093/jigpal/jzad006openalex-snapshot

Logics and collaboration

inferred← citeshttps://doi.org/10.1098/rsta.2020.0363openalex-snapshot

Artificial intelligence explainability: the technical and ethical dimensions

inferred← citeshttps://doi.org/10.1098/rstb.2022.0195openalex-snapshot

Bridging adaptive management and reinforcement learning for more robust decisions

inferred← citeshttps://doi.org/10.1101/2021.01.22.427799openalex-snapshot

REM: An Integrative Rule Extraction Methodology for Explainable Data Analysis in Healthcare

inferred← citeshttps://doi.org/10.1101/2021.01.25.21250459openalex-snapshot

REM: An Integrative Rule Extraction Methodology for Explainable Data Analysis in Healthcare

inferred← citeshttps://doi.org/10.1101/2023.03.20.533343openalex-snapshot

Deep learning for rapid analysis of cell divisions in vivo during epithelial morphogenesis and repair

inferred← citeshttps://doi.org/10.1101/2025.02.27.640661openalex-snapshot

SensitiveCancerGPT: Leveraging Generative Large Language Model on Structured Omics Data to Optimize Drug Sensitivity Prediction

inferred← citeshttps://doi.org/10.1101/2025.07.11.664302openalex-snapshot

XAI-based Data Visualization in Multimodal Medical Data

inferred← citeshttps://doi.org/10.1108/apjba-12-2024-0690openalex-snapshot

The impact of AI perceived transparency on trust in AI recommendations in healthcare applications

inferred← citeshttps://doi.org/10.1108/jices-06-2021-0059openalex-snapshot

Toward accountable human-centered AI: rationale and promising directions

inferred← citeshttps://doi.org/10.1108/jm2-01-2021-0032openalex-snapshot

Distributed model for customer churn prediction using convolutional neural network

inferred← citeshttps://doi.org/10.1109/5gwf52925.2021.00082openalex-snapshot

One Step Further: Tunable and Explainable Throughput Prediction based on Large-scale Commercial Networks

inferred← citeshttps://doi.org/10.1109/access.2021.3074676openalex-snapshot

Explainable Machine Learning for Default Privacy Setting Prediction

inferred← citeshttps://doi.org/10.1109/access.2021.3116664openalex-snapshot

Explainable Student Agency Analytics

inferred← citeshttps://doi.org/10.1109/access.2021.3127881openalex-snapshot

A Systematic Review of Human–Computer Interaction and Explainable Artificial Intelligence in Healthcare With Artificial Intelligence Techniques

inferred← citeshttps://doi.org/10.1109/access.2022.3175816openalex-snapshot

Adversary-Aware Multimodal Neural Networks for Cancer Susceptibility Prediction From Multiomics Data

inferred← citeshttps://doi.org/10.1109/access.2022.3178382openalex-snapshot

Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Challenges and Solutions

inferred← citeshttps://doi.org/10.1109/access.2022.3204171openalex-snapshot

Explainable Artificial Intelligence in CyberSecurity: A Survey

inferred← citeshttps://doi.org/10.1109/access.2023.3278800openalex-snapshot

Automated Explainable Detection of Cyclic Alternating Pattern (CAP) Phases and Sub-Phases Using Wavelet-Based Single-Channel EEG Signals

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