Abstract: Implementations generally relate to a decision graph. In some implementations, a method includes receiving a graph, where the graph includes data criteria. The method further includes receiving a data structure, where the data structure includes input values. The method further includes applying the data criteria of the graph against the input values of the data structure. The method further includes computing an output value based on the applying of the data criteria against the input values.

Abstract: Systems and methods for neurodivergence-driven ambiguity detection and resolution are disclosed. In one embodiment, a method for neurodivergence-driven ambiguity detection and resolution may include: (1) receiving, by an ambiguity detection training computer program executed by an electronic device, a labeled dataset comprising data labeled by one or more neurodivergent individuals, wherein the data is labeled as clear or ambiguous; (2) training, by the ambiguity detection training computer program, an ambiguity detection engine using the labeled dataset to predict ambiguities in new data; and (3) deploying, by the ambiguity detection training computer program, the ambiguity detection engine to a computer program or system, wherein the ambiguity detection engine is configured to receive the new data, predict whether the new data is ambiguous, and present a modification to the new data based on the prediction.

Abstract: Implementations generally relate to a decision graph. In some implementations, a method includes receiving a graph, where the graph includes data criteria. The method further includes receiving a data structure, where the data structure includes input values. The method further includes applying the data criteria of the graph against the input values of the data structure. The method further includes computing an output value based on the applying of the data criteria against the input values.