The paper describes the lab on Sexism identification in social networks (EXIST 2023) that will be hosted at the CLEF 2023 conference. The lab consists of three tasks, two of which are continuation of EXIST 2022 (sexism detection and sexism categorization) and a third and novel one on source intention identification. For this edition new test and training data will be provided and some novelties are introduced in order to tackle two central problems of Natural Language Processing (NLP): bias and fairness. Firstly, the sampling and data gathering process will take into account different sources of bias in data: seed, temporal and user bias. During the annotation process we will also consider some sources of “label bias” that come from the social and demographic characteristics of the annotators. Secondly, we will adopt the “learning with disagreements” paradigm by providing datasets containing also pre-aggregated annotations, so that systems can make use of this information to learn from different perspectives. The general goal of the EXIST shared tasks is to advance the state of the art in online sexism detection and categorization, as well as investigating to what extent bias can be characterized in data and whether systems may take fairness decisions when learning from multiple annotations.