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Explainability and transparency of computations, as well as compliance with data-privacy requirements, presuppose an understanding of whether some input plays a non-trivial role in computing an output. However, difficulty in distinguishing between correlation and causality, along with possible correlations between inputs, makes determination of such accountability challenging. A flow definition is presented that can make this distinction. The definition enables the construction of accountability evidence to explain how an input is computed based on an output, in terms of the intermediate variables used. Intermediate variables can also serve as mediation points that fully block the flow from an input to an output. A connection between accountability and mediation is established by showing the role of mediation points for constructing accountability evidence.