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Bingyu Liu, Jianhong Ye, Siyuan Zhang
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Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI
10.1016/j.inffus.2019.12.012 · 2020
Counterfactual explanations for multivariate time series
2021
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Guiding the generation of counterfactual explanations through temporal background knowledge for predictive process monitoring
10.1007/s10618-025-01117-3 · 2025
Generating counterfactual explanations under temporal constraints
2025
Transformer architecture and attention mechanisms in genome data analysis: a comprehensive review
10.3390/biology12071033 · 2023
BERT: pre-training of deep bidirectional transformers for language understanding
2019
Predictive process monitoring
2022
Explainable AI (XAI): core ideas, techniques, and solutions
10.1145/3561048 · 2023
A survey of methods for explaining black box models
10.1145/3236009 · 2018
DiCE4EL: interpreting process predictions using a milestone-aware counterfactual approach
2021
Counterfactual explanations for predictive business process monitoring
2021
CREATED: generating viable counterfactual sequences for predictive process analytics
2023
Attention is not explanation
2019
Robust counterfactual explanations in machine learning: a survey
2024
Explaining transformer-based next activity prediction by using attention scores
10.1007/s44311-025-00018-4 · 2025
Comparison-based inverse classification for interpretability in machine learning
2018
A unified approach to interpreting model predictions
2017
PLC transition sequence identification based on logical reduction: Y. luo et al
10.1007/s11768-025-00263-9 · 2025
Interpretable transformer hawkes processes: unveiling complex interactions in social networks
2024
Explaining machine learning classifiers through diverse counterfactual explanations
2020
From anecdotal evidence to quantitative evaluation methods: a systematic review on evaluating explainable AI
10.1145/3583558 · 2023
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Similarity based information retrieval using levenshtein distance algorithm
2020
FACE: feasible and actionable counterfactual explanations
2020
Case level counterfactual reasoning in process mining
2021
Deep learning for predictive business process monitoring: review and benchmark
2021
“Why should I trust you?”: Explaining the predictions of any classifier
2016
Anchors: high-precision model-agnostic explanations
2018
A primer in BERTology: what we know about how BERT works
10.1162/tacl_a_00349 · 2020
Improving customer churn prediction: a study of counterfactual explanations using wachter’s method, growing spheres method, and genetic algorithms
2025
Generating feasible and plausible counterfactual explanations for outcome prediction of business processes
10.1109/tsc.2025.3609837 · 2025
Outcome-oriented predictive process monitoring: review and benchmark
10.1145/3301300 · 2019
BERT rediscovers the classical NLP pipeline
2019
Gower distance-based multivariate control charts for a mixture of continuous and categorical variables
10.1016/j.eswa.2013.08.068 · 2014
Counterfactual explanations and algorithmic recourses for machine learning: a review
10.1145/3677119 · 2024
Large language models for business process management: opportunities and challenges
2023
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Counterfactual explanations without opening the black box: automated decisions and the GDPR
2018
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CFDiff: a diffusion-based generative framework for efficient multiphysical field prediction in smart IoT
10.1109/jiot.2025.3647465 · doi-reference
Counterfactual explanations and algorithmic recourses for machine learning: a review
10.1145/3677119 · doi-reference
Gower distance-based multivariate control charts for a mixture of continuous and categorical variables
10.1016/j.eswa.2013.08.068 · doi-reference
Outcome-oriented predictive process monitoring: review and benchmark
10.1145/3301300 · doi-reference
Generating feasible and plausible counterfactual explanations for outcome prediction of business processes
10.1109/tsc.2025.3609837 · doi-reference
A primer in BERTology: what we know about how BERT works
10.1162/tacl_a_00349 · doi-reference
From anecdotal evidence to quantitative evaluation methods: a systematic review on evaluating explainable AI
10.1145/3583558 · doi-reference
PLC transition sequence identification based on logical reduction: Y. luo et al
10.1007/s11768-025-00263-9 · doi-reference
Explaining transformer-based next activity prediction by using attention scores
10.1007/s44311-025-00018-4 · doi-reference
A survey of methods for explaining black box models
10.1145/3236009 · doi-reference
Explainable AI (XAI): core ideas, techniques, and solutions
10.1145/3561048 · doi-reference
Transformer architecture and attention mechanisms in genome data analysis: a comprehensive review
10.3390/biology12071033 · doi-reference
Guiding the generation of counterfactual explanations through temporal background knowledge for predictive process monitoring
10.1007/s10618-025-01117-3 · doi-reference
Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI
10.1016/j.inffus.2019.12.012 · doi-reference