We present a general approach towards controllable societal biases in natural language generation (NLG). Building upon the idea of adversarial triggers, we develop a method to induce or avoid biases in generated text containing mentions of specified demographic groups. We then analyze two scenarios: 1) inducing  biases for one demographic and avoiding biases for another  , and 2) mitigating biases between demographic pairs (e. g., man and woman). 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. Furthermore, our mitigation experimentsexemplify our technique 's effectiveness at equalizing the amount of biases across demographics while simultaneously generating less negatively biased text overall.
We present a general approach towards controllable societal biases in natural language generation (NLG). Building upon the idea of adversarial triggers, we develop a method to induce societal biases in generated text when input prompts contain mentions of specific demographic groups. We then analyze two scenarios: 1) inducing negative biases for one demographic and positive biases for another demographic , and 2) equalizing biases between demographics. The former scenario enables us to detect 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 . The second scenario is useful for mitigating biases in downstream applications such as dialogue generation. In our experiments, the mitigation technique proves to be effective at equalizing the amount of biases across demographics while simultaneously generating less negatively biased text overall.
R
or avoid
societal
R
containing mentions of specified
when input prompts contain mentions of specific
A
negative
R
avoiding
positive
A
demographic
R
mitigating biases between demographic pairs (e. g., man and woman).
equalizing biases between demographics.
R
gives us a tool for detecting
enables us to detect
R
, 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
. 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
R
as a new dimension for bias evaluation, and 2) discover topics that correspond to demographic inequalities in generated text. Furthermore, our mitigation experimentsexemplify our technique 's effectiveness
. The second scenario is useful for mitigating biases in downstream applications such as dialogue generation. In our experiments, the mitigation technique proves to be effective