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Corpus callosum dysgenesis is one of the most common congenital neurological malformations. Despite being a clear and identifiable structural alteration of the brains white matter connectivity, the impact of corpus callosum dysgenesis on cognition and behavior has remained unclear. Here we build upon past clinical observations in the literature to define the clinical phenotype of corpus callosum dysgenesis better using unadjusted and adjusted group differences compared with a neurotypical sample on a range of social and cognitive measures that have been previously reported to be impacted by a corpus callosum dysgenesis diagnosis. Those with a diagnosis of corpus callosum dysgenesis (n = 22) demonstrated significantly higher persuadability, credulity, and insensitivity to social trickery than neurotypical (n = 86) participants, after controlling for age, sex, education, autistic-like traits, social intelligence, and general cognition. To explore this further, machine learning, utilizing a set neurotypical sample for training the normative covariance structure of our psychometric variables, was used to test whether these dimensions possessed the capability to discriminate between a test-set of neurotypical and corpus callosum dysgenesis participants. We found that participants with a diagnosis of corpus callosum dysgenesis were best classed within dimension space along the same axis as persuadability, credulity, and insensitivity to social trickery after controlling for age and sex, with Leave-One-Out-Cross-Validation across 250 training-set permutations providing a mean accuracy of 71.7 percent. These results have wide-reaching implications for a) the characterization of corpus callosum dysgenesis, and b) the role of the corpus callosum in social inference.