GAPS: Generality and Precision with Shapley Attribution

In an age of the growing use of Machine-learning, it has become an imperative task to be able to explain the processes behind the functions of many "black box"models. The explainability feature of artificial intelligence is key to building trust between humans and computers' algorithmic predictions. One of the main ways to generate this interpretability is through attribution methods, which produce importance values of each feature for a single instance in a dataset. There are many different ways of attribution for various Machine-learning models, including ones designed for specific models or "model agnostic"attribution methods - ones that do not require a specific model to achieve importance values. These attribution methods are valued because of their easily understood nature. While evaluation procedures exist such as generality and precision for rule-based explanation methods, these have not been used on attribution methods until recently. A recent experiment by Ratul et al. [1] proved that the two most popular local model-agnostic attribution methods, LIME and SHAP, have poor precision and generality. In this paper, we propose a new attribution method, the Generality and Precision Shapley Attributions (GAPS). To evaluate these models, we use the generality and precision equations used previously to evaluate the other models. We present our findings that GAPS produces higher generality and precision scores than the existing LIME and SHAP models.