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08/30/2023
survex: an R package for explaining machine learning survival models
Due to their flexibility and superior performance, machine learning mode...
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08/29/2023
Glocal Explanations of Expected Goal Models in Soccer
The expected goal models have gained popularity, but their interpretabil...
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08/22/2023
Exploration of the Rashomon Set Assists Trustworthy Explanations for Medical Data
The machine learning modeling process conventionally culminates in selec...
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08/02/2023
Multi-task learning for classification, segmentation, reconstruction, and detection on chest CT scans
Lung cancer and covid-19 have one of the highest morbidity and mortality...
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07/15/2023
Explainable AI with counterfactual paths
Explainable AI (XAI) is an increasingly important area of research in ma...
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06/30/2023
The Effect of Balancing Methods on Model Behavior in Imbalanced Classification Problems
Imbalanced data poses a significant challenge in classification as model...
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06/20/2023
SeFNet: Bridging Tabular Datasets with Semantic Feature Nets
Machine learning applications cover a wide range of predictive tasks in ...
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06/06/2023
Adversarial Attacks and Defenses in Explainable Artificial Intelligence: A Survey
Explainable artificial intelligence (XAI) methods are portrayed as a rem...
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05/18/2023
Prevention is better than cure: a case study of the abnormalities detection in the chest
Prevention is better than cure. This old truth applies not only to the p...
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04/12/2023
Towards Evaluating Explanations of Vision Transformers for Medical Imaging
As deep learning models increasingly find applications in critical domai...
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03/17/2023
Hospital Length of Stay Prediction Based on Multi-modal Data towards Trustworthy Human-AI Collaboration in Radiomics
To what extent can the patient's length of stay in a hospital be predict...
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03/12/2023
Challenges facing the explainability of age prediction models: case study for two modalities
The prediction of age is a challenging task with various practical appli...
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02/26/2023
Performance is not enough: a story of the Rashomon's quartet
Predictive modelling is often reduced to finding the best model that opt...
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02/25/2023
HADES: Homologous Automated Document Exploration and Summarization
This paper introduces HADES, a novel tool for automatic comparative docu...
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11/10/2022
Climate Policy Tracker: Pipeline for automated analysis of public climate policies
The number of standardized policy documents regarding climate policy and...
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08/23/2022
SurvSHAP(t): Time-dependent explanations of machine learning survival models
Machine and deep learning survival models demonstrate similar or even im...
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08/21/2022
Performance, Opaqueness, Consequences, and Assumptions: Simple questions for responsible planning of machine learning solutions
The data revolution has generated a huge demand for data-driven solution...
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06/14/2022
Explainable expected goal models for performance analysis in football analytics
The expected goal provides a more representative measure of the team and...
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01/27/2022
Consolidated learning – a domain-specific model-free optimization strategy with examples for XGBoost and MIMIC-IV
For many machine learning models, a choice of hyperparameters is a cruci...
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11/15/2021
LIMEcraft: Handcrafted superpixel selection and inspection for Visual eXplanations
The increased interest in deep learning applications, and their hard-to-...
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07/29/2021
MAIR: Framework for mining relationships between research articles, strategies, and regulations in the field of explainable artificial intelligence
The growing number of AI applications, also for high-stake decisions, in...
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05/28/2021
Do not explain without context: addressing the blind spot of model explanations
The increasing number of regulations and expectations of predictive mach...
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05/26/2021
Fooling Partial Dependence via Data Poisoning
Many methods have been developed to understand complex predictive models...
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05/12/2021
Kleister: Key Information Extraction Datasets Involving Long Documents with Complex Layouts
The relevance of the Key Information Extraction (KIE) task is increasing...
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04/14/2021
Enabling Machine Learning Algorithms for Credit Scoring – Explainable Artificial Intelligence (XAI) methods for clear understanding complex predictive models
Rapid development of advanced modelling techniques gives an opportunity ...
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04/07/2021
Triplot: model agnostic measures and visualisations for variable importance in predictive models that take into account the hierarchical correlation structure
One of the key elements of explanatory analysis of a predictive model is...
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04/01/2021
fairmodels: A Flexible Tool For Bias Detection, Visualization, And Mitigation
Machine learning decision systems are getting omnipresent in our lives. ...
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12/28/2020
dalex: Responsible Machine Learning with Interactive Explainability and Fairness in Python
The increasing amount of available data, computing power, and the consta...
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12/11/2020
Do not repeat these mistakes – a critical appraisal of applications of explainable artificial intelligence for image based COVID-19 detection
The sudden outbreak and uncontrolled spread of COVID-19 disease is one o...
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09/28/2020
Transparency, Auditability and eXplainability of Machine Learning Models in Credit Scoring
A major requirement for credit scoring models is to provide a maximally ...
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09/24/2020
Landscape of R packages for eXplainable Artificial Intelligence
The growing availability of data and computing power fuels the developme...
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08/30/2020
MementoML: Performance of selected machine learning algorithm configurations on OpenML100 datasets
Finding optimal hyperparameters for the machine learning algorithm can o...
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07/06/2020
Does imputation matter? Benchmark for predictive models
Incomplete data are common in practical applications. Most predictive ma...
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06/02/2020
Interpretable Meta-Measure for Model Performance
Measures for evaluation of model performance play an important role in M...
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05/01/2020
The Grammar of Interactive Explanatory Model Analysis
When analysing a complex system, very often an answer for one question r...
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03/04/2020
Kleister: A novel task for Information Extraction involving Long Documents with Complex Layout
State-of-the-art solutions for Natural Language Processing (NLP) are abl...
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02/11/2020
Towards better understanding of meta-features contributions
Meta learning is a difficult problem as the expected performance of a mo...
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02/11/2020
Lifting Interpretability-Performance Trade-off via Automated Feature Engineering
Complex black-box predictive models may have high performance, but lack ...
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02/07/2020
What Would You Ask the Machine Learning Model? Identification of User Needs for Model Explanations Based on Human-Model Conversations
Recently we see a rising number of methods in the field of eXplainable A...
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10/06/2019
Named Entity Recognition – Is there a glass ceiling?
Recent developments in Named Entity Recognition (NER) have resulted in b...
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08/24/2019
EPP: interpretable score of model predictive power
The most important part of model selection and hyperparameter tuning is ...
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07/09/2019
Model Development Process
Predictive modeling has an increasing number of applications in various ...
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07/03/2019
Clustering of Medical Free-Text Records Based on Word Embeddings
Is it true that patients with similar conditions get similar diagnoses? ...
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03/27/2019
iBreakDown: Uncertainty of Model Explanations for Non-additive Predictive Models
Explainable Artificial Intelligence (XAI) brings a lot of attention rece...
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03/27/2019
The Landscape of R Packages for Automated Exploratory Data Analysis
The increasing availability of large but noisy data sets with a large nu...
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02/28/2019
SAFE ML: Surrogate Assisted Feature Extraction for Model Learning
Complex black-box predictive models may have high accuracy, but opacity ...
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12/05/2018
Are you tough enough? Framework for Robustness Validation of Machine Comprehension Systems
Deep Learning NLP domain lacks procedures for the analysis of model robu...
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09/19/2018
auditor: an R Package for Model-Agnostic Visual Validation and Diagnostics
Machine learning models have spread to almost every area of life. They a...
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09/19/2018
auditor: an R Package for Model-Agnostic Visual Validation and Diagnostic
Machine learning has spread to almost every area of life. It is successf...
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09/11/2018
