With the rise of machine learning models in sensitive areas, such as sexism detection on social media platforms, the accuracy of these models is of paramount importance. There are many ongoing research and evaluation campaigns in this field, like EXIST and EDOS. For this task, it is important not only the accurate predictions of the model but also to generate explanations for those predictions. Because most datasets that are used in the studies have been annotated by humans, it is important to understand the factors that can influence them. Therefore, assessing the reliability of annotations made by humans becomes crucial to ensure the quality of the validation process. In this project, we aim to measure the influence of explanations generated by prediction systems on annotators' agreement and compare them with model predictions. Our innovation is about using explanation techniques to better understand both model and human reliability.

• prof. dr. Massimo Poesio
• Hadi Mohammadi
• dr. Pablo Mosteiro Romero