Although the maturity of technologies based on Artificial Intelligence (AI) is rather advanced nowadays, their adoption, deployment and application are not as wide as it could be expected. This could be attributed to many barriers, among which the lack of trust of users stands out. Accountability is a relevant factor to progress in this trustworthiness aspect, as it allows to determine the causes that derived a given decision or suggestion made by an AI system. This article focuses on the accountability of a specific branch of AI, statistical machine learning (ML), based on a semantic approach. FIDES, an ontology-based approach towards achieving the accountability of ML systems is presented, where all the relevant information related to a ML-based model is semantically annotated, from the dataset and model parametrisation to deployment aspects, to be exploited later to answer issues related to reproducibility, replicability, definitely, accountability. The feasibility of the proposed approach has been demonstrated in two scenarios, real-world energy efficiency and manufacturing, and it is expected to pave the way towards raising awareness about the potential of Semantic Technologies in different factors that may be key in the trustworthiness of AI-based systems.

尽管如今基于人工智能（AI）的技术已经相当成熟，但其采用、部署和应用并不像预期的那么广泛。这可能归因于许多障碍，其中最突出的是用户缺乏信任。问责制是在可信度方面取得进展的一个相关因素，因为它可以确定人工智能系统做出给定决策或建议的原因。本文重点讨论基于语义方法的人工智能特定分支——统计机器学习 (ML) 的责任。FIDES 是一种基于本体的方法，旨在实现 ML 系统的问责制，其中与基于 ML 的模型相关的所有相关信息都经过语义注释，从数据集和模型参数化到部署方面，以便稍后用于回答问题与可重复性、可复制性、绝对与问责制有关。该方法的可行性已在现实世界的能源效率和制造这两个场景中得到了证明，预计将为提高人们对语义技术在不同因素中潜力的认识铺平道路，这些因素可能是人工智能可信度的关键基于系统。