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In several observational contexts where different raters evaluate a set of items, it is common to assume that all raters draw their scores from the same underlying distribution. However, a plenty of scientific works have evidenced the relevance of individual variability in different type of rating tasks. A common distributional assumption in this setting is that random effects as independent and identically distributed from a normal with the mean parameter fixed to zero and unknown variance. The present work aims to overcome this strong assumption in the inter-rater agreement estimation by assigning a Dirichlet Process (DP) mixture as the random effects' prior distribution. A new semi-parametric index is proposed to quantify raters polarization in presence of group heterogeneity. The model is applied to a real context.