  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 and describing potential sources of bias and errors
in data and models, with a particular focus on bias in machine learning
pipelines. The next chapter outlines a taxonomy and methods of Explainable AI
as a way to justify predictions and control and improve the model. Then, as an
example of a laborious manual data inspection and bias discovery process, a
skin lesion dataset is manually examined. A Global Explanation for the Bias
Identification method is proposed as an alternative semi-automatic approach to
manual data exploration for discovering potential biases in data. Relevant
numerical methods and metrics are discussed for assessing the effects of the
identified biases on the model. Whereas identifying errors and bias is
critical, improving the model and reducing the number of flaws in the future is
an absolute priority. Hence, the second part of the thesis focuses on
mitigating the influence of bias on ML models. Three approaches are proposed
and discussed: Style Transfer Data Augmentation, Targeted Data Augmentations,
and Attribution Feedback. Style Transfer Data Augmentation aims to address
shape and texture bias by merging a style of a malignant lesion with a
conflicting shape of a benign one. Targeted Data Augmentations randomly insert
possible biases into all images in the dataset during the training, as a way to
make the process random and, thus, destroy spurious correlations. Lastly,
Attribution Feedback is used to fine-tune the model to improve its accuracy by
eliminating obvious mistakes and teaching it to ignore insignificant input
parts via an attribution loss. The goal of these approaches is to reduce the
influence of bias on machine learning models, rather than eliminate it
entirely.
