Counterfactual explanations (CEs) are emerging as a crucial tool in Explainable AI (XAI) for understanding model decisions. This research investigates the impact of various factors on the quality of CEs generated for classification tasks. We explore how inter-class distance, data imbalance, balancing techniques, the presence of biased classifiers, and decision thresholds influence CE quality. To answer these research questions, we conduct experiments on various datasets, classification models and counterfactual generators. The datasets include the MNIST and GMSC dataset. The models include well-established models like MLP and Random Forest, along with the novel NeuroTree model. The generators include the method proposed by Wachter et al. and the REVISE method. We evaluate how different factors affect CE quality by performing an extensive experimental analysis. Our findings demonstrate that increasing inter-class distance degrades CE quality, particularly explanation plausibility. Data imbalance showed minimal impact, while balancing techniques yielded a slight improvement in CE plausibility, especially for the minority class. Classifiers biased towards specific subgroups resulted in lower CE quality for those subgroups. We observed limited evidence for a consistent amplification effect of decision thresholds. This research utilizes various datasets and classification models, including the novel NeuroTree model. Our findings contribute to XAI by providing insights into factors affecting CE quality and highlighting areas for future development, particularly regarding fairness and handling imbalanced data.

The continuous generation of a large volume of health data from different sources has led to healthcare being a data-intensive domain. To achieve innovative advances in medical treatment procedures and to provide personalized healthcare services to the patients this data needs to be shared among different medical facilities. However, because this data is highly sensitive and personal, several challenges can be faced. Since blockchain technology has features such as transparency, immutability, confidentiality, and auditability, research is being performed to check whether it can be integrated in the healthcare system and thus in the medical data sharing. Nonetheless, privacy is an important aspect of healthcare systems that blockchain technology needs improvement in.
This research, first defines the security and privacy requirements for healthcare systems. Then, we look deeper into the privacy requirements that have to be met in blockchain systems and the threats that can arise when systems do not meet them. Next, we present several privacy protection techniques that can be used in a blockchain-based healthcare system and present a design which is a combination of techniques that fulfill the privacy requirements. Lastly, this design is evaluated to see how each component of the design fulfills the requirements necessary.