2 citations · 4 across the 5 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,"…
Data augmentation and explainability for bias discovery and mitigation in deep learning
Agnieszka Mikołajczyk-Bareła
This dissertation explores the impact of bias in deep neural networks and presents methods for reducing its influence on model performance. The first part begins by categorizing an…
On the Importance of Sign Labeling: The Hamburg Sign Language Notation System Case Study
Maria Ferlin, Sylwia Majchrowska, Marta Plantykow +4
Labeling is the cornerstone of supervised machine learning, which has been exploited in a plethora of various applications, with sign language recognition being one of them. Howeve…
Keyword Extraction from Short Texts with a Text-To-Text Transfer Transformer
Piotr Pęzik, Agnieszka Mikołajczyk-Bareła, Adam Wawrzyński +2
The paper explores the relevance of the Text-To-Text Transfer Transformer language model (T5) for Polish (plT5) to the task of intrinsic and extrinsic keyword extraction from short…