This post intends to propose a technique termed as Dual Coding for testing or performing quality control checks on Machine Learning models from quality assurance (QA) perspective. This could be useful in performing black box testing of ML models.
The proposed technique is based on the principles of Dual Coding Theory (DCT) hypothesized by Allan Paivio of the University of Western Ontario in 1971. According to Dual Coding Theory, our brain uses two different systems including verbal and non-verbal/visual to the gather, process, store, and retrieve (recall) the information related to a particular subject. One of the key assumptions of dual coding theory is the connections (also termed as referential connections) that link verbal and nonverbal representations into a complex associative network. For example, let's say we are shown flower images and also told about the name of these flowers (such as rose, lotus etc). At a later point in time, when told about one of these flowers by name, or shown one of the images, we end up classifying them as flowers. Pay attention to the fact of one of the two systems (verbal or non-verbal/visual) get activated appropriately to classify the subject (word or images) in the correct manner. The following diagram represents different representations of a dual-coding theory.
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