36 citations · 98 across the 9 of their papers we have counts for
9 papers
Enabling Machine Learning Algorithms for Credit Scoring -- Explainable Artificial Intelligence (XAI) methods for clear understanding complex predictive models
Przemysław Biecek, Marcin Chlebus, Janusz Gajda +5
Rapid development of advanced modelling techniques gives an opportunity to develop tools that are more and more accurate. However as usually, everything comes with a price and in t…
Transparency, Auditability and eXplainability of Machine Learning Models in Credit Scoring
Michael Bücker, Gero Szepannek, Alicja Gosiewska +1
A major requirement for credit scoring models is to provide a maximally accurate risk prediction. Additionally, regulators demand these models to be transparent and auditable. Thus…
Landscape of R packages for eXplainable Artificial Intelligence
Szymon Maksymiuk, Alicja Gosiewska, Przemyslaw Biecek
The growing availability of data and computing power fuels the development of predictive models. In order to ensure the safe and effective functioning of such models, we need metho…
Interpretable Meta-Measure for Model Performance
Alicja Gosiewska, Katarzyna Woźnica, Przemysław Biecek
Benchmarks for the evaluation of model performance play an important role in machine learning. However, there is no established way to describe and create new benchmarks. What is m…
Lifting Interpretability-Performance Trade-off via Automated Feature Engineering
Alicja Gosiewska, Przemyslaw Biecek
Complex black-box predictive models may have high performance, but lack of interpretability causes problems like lack of trust, lack of stability, sensitivity to concept drift. On…
EPP: interpretable score of model predictive power
Alicja Gosiewska, Mateusz Bakala, Katarzyna Woznica +2
The most important part of model selection and hyperparameter tuning is the evaluation of model performance. The most popular measures, such as AUC, F1, ACC for binary classificati…
Do Not Trust Additive Explanations
Alicja Gosiewska, Przemyslaw Biecek
Explainable Artificial Intelligence (XAI)has received a great deal of attention recently. Explainability is being presented as a remedy for the distrust of complex and opaque model…
SAFE ML: Surrogate Assisted Feature Extraction for Model Learning
Alicja Gosiewska, Aleksandra Gacek, Piotr Lubon +1
Complex black-box predictive models may have high accuracy, but opacity causes problems like lack of trust, lack of stability, sensitivity to concept drift. On the other hand, inte…
auditor: an R Package for Model-Agnostic Visual Validation and Diagnostics
Alicja Gosiewska, Przemyslaw Biecek
Machine learning models have spread to almost every area of life. They are successfully applied in biology, medicine, finance, physics, and other fields. With modern software it is…