4 citations · 4 across the 4 of their papers we have counts for
5 papers
Targeted Data Augmentation for bias mitigation
Agnieszka Mikołajczyk-Bareła, Maria Ferlin, Michał Grochowski
The development of fair and ethical AI systems requires careful consideration of bias mitigation, an area often overlooked or ignored. In this study, we introduce a novel and effic…
A survey on bias in machine learning research
Agnieszka Mikołajczyk-Bareła, Michał Grochowski
Current research on bias in machine learning often focuses on fairness, while overlooking the roots or causes of bias. However, bias was originally defined as a "systematic error,"…
Review of methods for automatic cerebral microbleeds detection
Maria Ferlin, Zuzanna Klawikowska, Michał Grochowski +2
Cerebral microbleeds detection is an important and challenging task. With the gaining popularity of the MRI, the ability to detect cerebral microbleeds also raises. Unfortunately,…
Towards explainable classifiers using the counterfactual approach -- global explanations for discovering bias in data
Agnieszka Mikołajczyk, Michał Grochowski, Arkadiusz Kwasigroch
The paper proposes summarized attribution-based post-hoc explanations for the detection and identification of bias in data. A global explanation is proposed, and a step-by-step fra…
Style transfer-based image synthesis as an efficient regularization technique in deep learning
Agnieszka Mikołajczyk, Michał Grochowski
These days deep learning is the fastest-growing area in the field of Machine Learning. Convolutional Neural Networks are currently the main tool used for image analysis and classif…