Fairness (language acquisition) is a concept within computational linguistics and related fields, such as machine learning and artificial intelligence ethics (and listed parent concepts including computer vision), that investigates the equitable development and performance of computational models designed to simulate or facilitate language acquisition. It specifically addresses the potential for bias in language learning systems, examining how training data, algorithmic choices, or evaluation methods might lead to unfair outcomes. This includes disparities in model performance, learned linguistic representations, or the acquisition of specific language skills across different demographic groups, linguistic varieties, or sensitive attributes. Research in fairness (language acquisition) seeks to identify sources of bias in the language learning pipeline and develop methodologies to mitigate these biases, ensuring that language acquisition models are robust, reliable, and equitable for all users and linguistic contexts, thereby contributing to the development of trustworthy AI systems.