  In this paper, we propose a novel gender bias detection method by utilizing
attention map for transformer-based models. We 1) give an intuitive gender bias
judgement method by comparing the different relation degree between the genders
and the occupation according to the attention scores, 2) design a gender bias
detector by modifying the attention module, 3) insert the gender bias detector
into different positions of the model to present the internal gender bias flow,
and 4) draw the consistent gender bias conclusion by scanning the entire
Wikipedia, a BERT pretraining dataset. We observe that 1) the attention
matrices, Wq and Wk introduce much more gender bias than other modules
(including the embedding layer) and 2) the bias degree changes periodically
inside of the model (attention matrix Q, K, V, and the remaining part of the
attention layer (including the fully-connected layer, the residual connection,
and the layer normalization module) enhance the gender bias while the averaged
attentions reduces the bias).
