The presence of Artificial Intelligence (AI) in our society is increasing, which brings with it the need to understand the behaviour of AI mechanisms, including machine learning predictive algorithms fed with tabular data, text, or images, among other types of data. This work focuses on interpretability of predictive models based on functional data. Designing interpretability methods for functional data models implies working with a set of features whose size is infinite. In the context of scalar on function regression, we propose an interpretability method based on the Shapley value for continuous games, a mathematical formulation that allows to fairly distribute a global payoff among a continuous set players. The method is illustrated through a set of experiments with simulated and real data sets. The open source Python package ShapleyFDA is also presented.

翻译：人工智能（AI）在社会中的存在日益增强，这带来了理解AI机制行为的需求，包括基于表格数据、文本或图像等多种数据类型训练的机器学习预测算法。本研究聚焦于基于功能数据的预测模型的可解释性。为功能数据模型设计可解释性方法意味着需要处理特征集大小为无限的情况。在标量对函数回归的背景下，我们提出了一种基于连续博弈Shapley值的可解释性方法，该数学公式能够将全局收益公平地分配给连续参与者集合。通过一系列模拟和真实数据集的实验对该方法进行了说明。同时介绍了开源Python软件包ShapleyFDA。