More than Minorities and Majorities: Understanding Multilateral Bias in Language Generation (2024)

Abstract: Pretrained models learned from real corpora can often capture undesirable features, leading to bias issues against different demographic groups. Most existing studies on bias dataset construction or bias mitigation methods only focus on one demographic group pair to study a certain bias, e.g. black vs. white for racial bias. However, in real-world applications, there are more than two demographic groups that are at risk of the same bias. In this paper, we propose to analyze and reduce biases across multiple demographic groups. We collect and build a multi-demographic bias dataset including five commonly discussed bias dimensions. To mitigate multi-demographic bias, we adopt several novel debiasing methods, including regularisation-based and augmentation-based methods, as well as appropriate evaluation metrics for multi-demographic bias measurement. Experimental results on the proposed multi-demographic dataset show that a fairer model can be achieved using a multi-demographic debiasing approach. Also, the model debiased using the proposed multi-demographic debiasing methods can better transfer to unseen demographics without sacrificing the performance of the pretrained model.

## introduction increasing attention has been paid to research on social biases in language generation. recently, largescale pretrained models @xcite @xcite based on the transformer architecture @xcite have achieved great success in many natural language processing tasks. many of these pretrained models have been applied in natural language generation systems and shown promising performance, such as dialogue generation @xcite , machine translation @xcite , ai agents @xcite ## multi-demographic bias dataset we retrieved data based on multiple demographic groups instead of one when collecting data, so our dataset contains a broader range of biases. to avoid any additional interference in the experiment due to different domains, we also extract data from reddit foot_1 to form the dataset. we construct the dataset by the following steps we construct five b multi utilizing the target terms and attribute terms in @xcite to define five bias dimensions. as shown in there are three steps in data collection: 1) retrieving examples according to the b multi ; 2) cleaning the retrieved examples; and 3) extracting examples that may contain biased parts. specifically, we first retrieve the candidate sentences for each demographic. we use pushshift api foot_2 with the target items as queries to retrieve relevant comments on reddit. we retrieve relevant examples for the three years prior to march 10, 2022. second, we remove links, emojis, and extra white spaces in the retrieved sentences and lowercase all letters. third, to get more biased examples, we kept the seven words before and after the target words as the final candidate examples @xcite . ## multi-demographic bias evaluation following @xcite , we quantify the bias based on multiple perplexity distributions for multi-demographic groups. we use dialogpt as a conversational language model to study the bias. for each bias dimension, we apply the model to every demographic test data. the performance of a fair model on test data of different demographic groups should be close. given this, we use differences in the model's performance over test datasets to quantify the bias. specifically, to analyze the model's bias against multiple demographic groups more comprehensively and clearly, we utilize analysis of variance (anova) @xcite to evaluate the bias degree in the model. anova is used to compare variances across the means or averages of multiple groups. the result of anova is the "f-statistic", which is calculated as follows: furthermore, inspired by barikeri et al., we use the student's two-tailed test method to quantify bias between two demographic groups. we also report the bias effects in paired demographic groups by the "t-values" of the student's two-tailed test. a fair model generates similar results for each demographic group, and the "t-value" should be close to 0 (we set α=0.05 as @xcite ). ## multi-demographic bias mitigation we test and report the performance of bias mitigation methods on the dialogpt. to simultaneously reduce the bias of language models against multiple demographic groups not just pairwise demographic groups, following @xcite , we designed new bias mitigating methods based on some traditional debiasing strategies. we describe these debiasing approaches into two categories: loss-based approaches (lmd, add) and data augmentation approaches (ctda, cada). ## conclusion in this paper, we focus on five bias dimensions in language generation. to further strengthen the research integrity of bias in natural language processing, we also analyze the age bias in the dialogue generation model. to study the multi-demographic biases, we provide a dataset, bias evaluation metrics, and bias mitigation methods. experiments on our dataset demonstrate that our debiasing methods can mitigate biases among multi-demographics effectively. our debiasing methods have better transfer ability than traditional methods among unknown demographic groups.