Rapid advancements in artificial intelligence (AI) technology have brought about a plethora of new challenges in terms of governance and regulation. AI systems are being integrated into various industries and sectors, creating a demand from decision-makers to possess a comprehensive and nuanced understanding of the capabilities and limitations of these systems. One critical aspect of this demand is the ability to explain the results of machine learning models, which is crucial to promoting transparency and trust in AI systems, as well as fundamental in helping machine learning models to be trained ethically. In this paper, we present novel metrics to quantify the degree of which AI model predictions can be easily explainable by its features. Our metrics summarize different aspects of explainability into scalars, providing a more comprehensive understanding of model predictions and facilitating communication between decision-makers and stakeholders, thereby increasing the overall transparency and accountability of AI systems.

翻译：人工智能（AI）技术的快速发展带来了治理与监管方面的一系列新挑战。AI系统正被整合到各行业领域，决策者们需要全面且深入地理解这些系统的能力与局限性。这一需求的核心在于能够解释机器学习模型的结果——这对于提升AI系统的透明度和可信度至关重要，也是确保机器学习模型在符合伦理规范下训练的基础。本文提出了新颖的量化指标，以评估AI模型预测结果被其特征解释的难易程度。我们的指标将可解释性的不同维度概括为标量值，从而提供对模型预测更全面的理解，促进决策者与利益相关者之间的沟通，进而提升AI系统的整体透明度与责任性。