Before revision:  The former scenario gives us a tool for detecting the types of biases present in the model , and the latter is useful for mitigating biases in downstream applications (e. g., dialogue generation). Specifically, our approach facilitates more explainable biases by allowing us to 1) use the relative effectiveness of inducing biases for different demographics as a new dimension for bias evaluation, and 2) discover topics that correspond to demographic inequalities in generated text.
After revision:  The former scenario gives us a tool for detecting the types of biases present in the model . Specifically, we show the effectiveness of our approach at facilitating bias analysis by finding topics that correspond to demographic inequalities in generated text and comparing the relative effectiveness of inducing biases for different demographics as a new dimension for bias evaluation, and 2) discover topics that correspond to demographic inequalities in generated text.