eXplainable Artificial Intelligence (XAI) is a rapidly progressing field of machine learning, aiming to unravel the predictions of complex models. XAI is especially required in sensitive applications, e.g. in health care, when diagnosis, recommendations and treatment choices might rely on the decisions made by artificial intelligence systems. AI approaches have become widely used in aging research as well, in particular, in developing biological clock models and identifying biomarkers of aging and age-related diseases. However, the potential of XAI here awaits to be fully appreciated. We discuss the application of XAI for developing the "aging clocks" and present a comprehensive analysis of the literature categorized by the focus on particular physiological systems.

翻译：可解释人工智能（XAI）是机器学习领域一个快速发展的分支，旨在解析复杂模型的预测机制。在诊断、建议和治疗选择可能依赖人工智能系统决策的敏感应用中（例如医疗保健领域），XAI尤为重要。人工智能方法已广泛应用于衰老研究，尤其是在开发生物时钟模型以及识别衰老和年龄相关疾病的生物标志物方面。然而，XAI在此领域的潜力仍有待充分发掘。本文探讨了XAI在开发"衰老时钟"中的应用，并根据关注特定生理系统的文献进行了全面分析。