  Recent work has shown that distributional word vector spaces often encode
human biases like sexism or racism. In this work, we conduct an extensive
analysis of biases in Arabic word embeddings by applying a range of recently
introduced bias tests on a variety of embedding spaces induced from corpora in
Arabic. We measure the presence of biases across several dimensions, namely:
embedding models (Skip-Gram, CBOW, and FastText) and vector sizes, types of
text (encyclopedic text, and news vs. user-generated content), dialects
(Egyptian Arabic vs. Modern Standard Arabic), and time (diachronic analyses
over corpora from different time periods). Our analysis yields several
interesting findings, e.g., that implicit gender bias in embeddings trained on
Arabic news corpora steadily increases over time (between 2007 and 2017). We
make the Arabic bias specifications (AraWEAT) publicly available.
