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Text representation models are prone to exhibit a range of societal biases,reflecting the non-controlled and biased nature of the underlying pretrainingdata, which consequently leads to severe ethical issues and even biasamplification. Recent work has predominantly focused on measuring andmitigating bias in pretrained language models. Surprisingly, the landscape ofbias measurements and mitigation resources and methods for conversationallanguage models is still very scarce: it is limited to only a few types ofbias, artificially constructed resources, and completely ignores the impactthat debiasing methods may have on the final performance in dialog tasks, e.g.,conversational response generation. In this work, we present RedditBias, thefirst conversational data set grounded in the actual human conversations fromReddit, allowing for bias measurement and mitigation across four important biasdimensions: gender, race, religion, and queerness. Further, we develop anevaluation framework which simultaneously 1) measures bias on the developedRedditBias resource, and 2) evaluates model capability in dialog tasks aftermodel debiasing. We use the evaluation framework to benchmark the widely usedconversational DialoGPT model along with the adaptations of four debiasingmethods. Our results indicate that DialoGPT is biased with respect to religiousgroups and that some debiasing techniques can remove this bias while preservingdownstream task performance.

RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Models