From Confusion to Clarity: ProtoScore - A Framework for Evaluating Prototype-Based XAI

ProtoScore represents a significant advancement in the field of explainable artificial intelligence (XAI), particularly in addressing the challenges posed by the complexity and opacity of neural networks in critical sectors such as healthcare, finance, and law. The lack of standardized benchmarks has hindered the objective evaluation of prototype-based XAI methods, leading to subjective assessments that can undermine trust in AI systems. By establishing a robust framework for assessing these methods, ProtoScore aims to fill this gap, facilitating fair and comprehensive evaluations across various data types, with a specific focus on time series data. This initiative not only enhances the understanding of AI decision-making processes but also promotes the validation of fairness in outcomes, which is crucial for fostering appropriate trust in AI technologies.