Annotator Disagreement

Annotator disagreement, the inconsistency in human judgments on tasks like text classification or sentiment analysis, is a significant challenge in natural language processing. Current research focuses on understanding the sources of this disagreement (e.g., ambiguity, subjective interpretations) and developing methods to mitigate its impact on model training, such as creating annotator embeddings to represent individual biases and perspectives within models. These techniques, often employing transformer-based architectures, aim to improve model performance and fairness by explicitly accounting for human variability rather than simply aggregating potentially conflicting labels. Addressing annotator disagreement is crucial for building more robust and reliable AI systems.