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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.
Review of methods for automatic cerebral microbleeds detection
Cerebral microbleeds detection is an important and challenging task.
Towards explainable classifiers using the counterfactual approach -- global explanations for discovering bias in data
The paper proposes summarized attribution-based post-hoc explanations for the detection and identification of bias in data.
Style transfer-based image synthesis as an efficient regularization technique in deep learning
In a proposed approach, the newly created images are described with pseudo-labels, and then used as a training dataset.
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