  All AI models are susceptible to learning biases in data that they are
trained on. For generative dialogue models, being trained on real human
conversations containing unbalanced gender and race/ethnicity references can
lead to models that display learned biases, which we define here broadly as any
measurable differences in the distributions of words or semantic content of
conversations based on demographic groups. We measure the strength of such
biases by producing artificial conversations between two copies of a dialogue
model, conditioning one conversational partner to state a name commonly
associated with a certain gender and/or race/ethnicity. We find that larger
capacity models tend to exhibit more gender bias and greater stereotyping of
occupations by gender. We show that several methods of tuning these dialogue
models, specifically name scrambling, controlled generation, and unlikelihood
training, are effective in reducing bias in conversation, including on a
downstream conversational task. Name scrambling is also effective in lowering
differences in token usage across conversations where partners have names
associated with different genders or races/ethnicities.
