The rise of AI in human contexts places new demands on automated systems to be transparent and explainable. We examine some anthropomorphic ideas and principles relevant to such accountablity in order to develop a theoretical framework for thinking about digital systems in complex human contexts and the problem of explaining their behaviour. Structurally, systems are made of modular and hierachical components, which we abstract in a new system model using notions of modes and mode transitions. A mode is an independent component of the system with its own objectives, monitoring data, and algorithms. The behaviour of a mode, including its transitions to other modes, is determined by functions that interpret each mode's monitoring data in the light of its objectives and algorithms. We show how these belief functions can help explain system behaviour by visualising their evaluation as trajectories in higher-dimensional geometric spaces. These ideas are formalised mathematically by abstract and concrete simplicial complexes. We offer three techniques: a framework for design heuristics, a general system theory based on modes, and a geometric visualisation, and apply them in three types of human-centred systems.

翻译：人工智能在人类语境中的兴起，对自动化系统的透明性与可解释性提出了新要求。我们考察了与这种可问责性相关的拟人化理念与原则，旨在构建一个理论框架，用以思考数字系统在复杂人类语境中的行为解释问题。从结构上看，系统由模块化与层次化组件构成，我们通过模态（mode）及其转换（mode transition）概念，将其抽象为一种新系统模型。模态是系统的独立组件，拥有自身目标、监测数据与算法。模态的行为（包括向其他模态的转换）由解释函数决定，这些函数基于模态的目标与算法对其监测数据进行解读。我们展示了这些信念函数（belief function）如何通过将评估结果可视化为高维几何空间中的轨迹，来解释系统行为。这些思想通过抽象与具体单纯复形（simplicial complex）以数学形式化表达。我们提出了三种技术：设计启发式框架、基于模态的通用系统理论以及几何可视化方法，并将其应用于三类以人为中心的系统中。