On the Impact of Neighbourhood Sampling to Satisfy Sufficiency and Necessity Criteria in Explainable AI

In the context of Machine Learning(ML) and Artificial Intelligence (AI), the concepts of sufficiency and necessity of features offer nuanced perspectives on the cause-and-effect relationships underlying a model’s outputs. These concepts are, therefore, essential in Explainable AI (XAI) as they can provide a more holistic understanding of a “black-box” AI model. Addressing this need, our study explored the relationships between the XAI’s explanations and the sufficiency and necessity of features in data. This is achieved by emphasising the impact of neighbourhoods, which are central in generating explanations. By analysing a diverse set of neighbourhoods, we highlighted how they influence the alignment between the feature rankings by XAI and the measures of sufficiency and necessity. This work offers two contributions. First, it provides a comprehensive discussion on how XAI frameworks relate to sufficiency and necessity with respect to their operating neighbourhoods; and second, it empirically demonstrates the effectiveness of these neighbourhoods in conveying the sufficiency and necessity of features by the XAI frameworks.