The project will explore how NLP approaches can be used to study and assess the quality of data annotations. We want to investigate potential bias in human annotations stemming from domain knowledge, annotation task setup, annotation codes, etc. Two main approaches include leveraging external text embedding models for discovering discrepancies as well as modelling different strata of data to assess the homogeneity and transferability of human-assigned labels.