Explainability of AI-predictions based on psychological profiling

Abstract Using a local surrogate approach from explainable AI, a new prediction method for the performance of start-up companies based on psychological profiles is proposed. The method assumes the existence of an interpreted ‘base model’, the predictions of which are enhanced by an AI-model delivering corrections that improve the overall accuracy. The surrogate (proxi) models the difference between the original (labeled) data and the data with labels replaced by the AI-corrections. As this corresponds to comparing the AI-correction applied before the base model is used with the original utilisation of the base model, the approach is called Before and After prediction Parameter Comparison (BAPC). The change of the base model under application of the AI-correction yields an interpretation of it by means of ‘effective’ parameter changes. This is useful for the interpretation of ‘subjective’ psychological profiles (such as ‘risk-affinity’, ‘open-mindedness’, etc.) in terms of effective changes of ‘objective’ monetary firm data (such as ‘revenue’, ‘price of product’, or ‘cost of development’).