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Found 5 papers, 1 papers with code
A survey on bias in machine learning research
Current research on bias in machine learning often focuses on fairness, while overlooking the roots or causes of bias.
Targeted Data Augmentation for bias mitigation
The development of fair and ethical AI systems requires careful consideration of bias mitigation, an area often overlooked or ignored.
Data augmentation and explainability for bias discovery and mitigation in deep learning
Three approaches are proposed and discussed: Style Transfer Data Augmentation, Targeted Data Augmentations, and Attribution Feedback.
On the Importance of Sign Labeling: The Hamburg Sign Language Notation System Case Study
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.
Keyword Extraction from Short Texts with a Text-To-Text Transfer Transformer
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 text passages.
