Distributed representations of words in the form of word embeddings Pennington et al., 2014) and contextualized word embeddings (Peters et al., 2018; Devlin et al., 2018; Radford et al., 2018; McCann et al., 2017; Radford et al., 2019) have led to huge performance improvement on many NLP tasks. However, several recent studies show that training word embeddings in large corpora could lead to encoding societal biases present in these human-produced data<cite> (Bolukbasi et al., 2016</cite>; Caliskan et al., 2017) . In this work, we extend these analyses to the ELMo contextualized word embeddings. Our work provides a new intrinsic analysis of how ELMo represents gender in biased ways.