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.

翻译：本论文探讨深度神经网络中偏差的影响，并提出降低其对模型性能作用的方法。第一部分首先对数据和模型中潜在的偏差与错误来源进行分类与描述，重点关注机器学习流程中的偏差。随后章节概述了可解释人工智能的分类体系与方法，作为验证预测结果、控制和优化模型的手段。接着，以皮肤病变数据集为例，通过人工方式对该数据集进行了手动检查与偏差发现过程演示。本文提出了一种用于偏差识别的全局解释方法，作为替代半自动数据探索以发现数据中潜在偏差的方案。相关数值方法与度量指标被用于评估已识别偏差对模型的影响。尽管识别错误与偏差至关重要，但改进模型并减少未来缺陷仍是首要任务。因此，论文第二部分聚焦于缓解偏差对机器学习模型的影响。本文提出并讨论了三种方法：风格迁移数据增强、定向数据增强与归因反馈。风格迁移数据增强通过将恶性病变的纹理与良性病变的冲突形状相融合，旨在解决形状与纹理偏差。定向数据增强在训练过程中向数据集所有图像随机注入可能的偏差，通过随机化过程破坏虚假相关性。最后，归因反馈通过消除明显错误并利用归因损失使模型忽略无关输入部分，对模型进行微调以提升其准确性。这些方法的目标是降低偏差对机器学习模型的影响，而非完全消除偏差。