Everyday life is increasingly influenced by artificial intelligence, and there is no question that machine learning algorithms must be designed to be reliable and trustworthy for everyone. Specifically, computer scientists consider an artificial intelligence system safe and trustworthy if it fulfills five pillars: explainability, robustness, transparency, fairness, and privacy. In addition to these five, we propose a sixth fundamental aspect: conformity, that is, the probabilistic assurance that the system will behave as the machine learner expects. In this paper, we propose a methodology to link conformal prediction with explainable machine learning by defining CONFIDERAI, a new score function for rule-based models that leverages both rules predictive ability and points geometrical position within rules boundaries. We also address the problem of defining regions in the feature space where conformal guarantees are satisfied by exploiting techniques to control the number of non-conformal samples in conformal regions based on support vector data description (SVDD). The overall methodology is tested with promising results on benchmark and real datasets, such as DNS tunneling detection or cardiovascular disease prediction.

翻译：日常生活日益受到人工智能的影响，毋庸置疑，机器学习算法必须设计得对每个人都可靠且值得信赖。具体而言，计算机科学家认为，若人工智能系统满足可解释性、鲁棒性、透明性、公平性和隐私性这五大支柱，则是安全且值得信赖的。除这五项外，我们提出第六个基本方面：一致性，即系统行为符合机器学习者期望的概率性保证。本文提出一种将保形预测与可解释机器学习相连接的方法，通过定义CONFIDERAI——一种基于规则模型的新型评分函数，该函数同时利用规则的预测能力以及点在规则边界内的几何位置。此外，我们基于支持向量数据描述技术，通过控制保形区域中非保形样本数量，解决了在特征空间中定义满足保形保证的区域问题。整体方法在基准数据集和真实数据集（如DNS隧道检测或心血管疾病预测）上进行了测试，结果令人鼓舞。