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Digital Twins (DT) are essentially dynamic data-driven models that serve asreal-time symbiotic "virtual replicas" of real-world systems. DT can leveragefundamentals of Dynamic Data-Driven Applications Systems (DDDAS) bidirectionalsymbiotic sensing feedback loops for its continuous updates. Sensing loops canconsequently steer measurement, analysis and reconfiguration aimed at moreaccurate modelling and analysis in DT. The reconfiguration decisions can beautonomous or interactive, keeping human-in-the-loop. The trustworthiness ofthese decisions can be hindered by inadequate explainability of the rationale,and utility gained in implementing the decision for the given situation amongalternatives. Additionally, different decision-making algorithms and modelshave varying complexity, quality and can result in different utility gained forthe model. The inadequacy of explainability can limit the extent to whichhumans can evaluate the decisions, often leading to updates which are unfit forthe given situation, erroneous, compromising the overall accuracy of the model.The novel contribution of this paper is an approach to harnessingexplainability in human-in-the-loop DDDAS and DT systems, leveragingbidirectional symbiotic sensing feedback. The approach utilises interpretablemachine learning and goal modelling to explainability, and considers trade-offanalysis of utility gained. We use examples from smart warehousing todemonstrate the approach.

Explainable Human-in-the-loop Dynamic Data-Driven Digital Twins