Toward Annotator Group Bias in Crowdsourcing (2022)

Abstract: Crowdsourcing has emerged as a popular approach for collecting annotated data to train supervised machine learning models. However, annotator bias can lead to defective annotations. Though there are a few works investigating individual annotator bias, the group effects in annotators are largely overlooked. In this work, we reveal that annotators within the same demographic group tend to show consistent group bias in annotation tasks and thus we conduct an initial study on annotator group bias. We first empirically verify the existence of annotator group bias in various real-world crowdsourcing datasets. Then, we develop a novel probabilistic graphical framework GroupAnno to capture annotator group bias with an extended Expectation Maximization (EM) algorithm. We conduct experiments on both synthetic and real-world datasets. Experimental results demonstrate the effectiveness of our model in modeling annotator group bias in label aggregation and model learning over competitive baselines.

## introduction the performance of supervised machine learning algorithms heavily relies on the quality of the annotated training data. due to the heavy workload of annotation tasks, researchers and practitioners typically take advantage of crowdsourcing platforms to obtain cost-effective annotation data @xcite . however, the labels collected from multiple crowdsourcing annotators could be not consistent, since the expertise and reliability of the annotators are uncertain, and the task itself could be subjective and difficult. in recent years, a lot of efforts from the machine learning community have been conducted to mitigate the effect of these noisy crowdsourcing labels @xcite . various approaches have been proposed to model the quality @xcite , confidence @xcite , expertise @xcite , reliability @xcite of annotators; or model the difficulty of the tasks @xcite . with such information, we can infer the truth label from the noisy labels more accurately and correspondingly train a more desirable model. in terms of annotator modeling, existing studies mainly concentrated on factors like quality, confidence, expertise, etc., which could affect the annotation results. besides, the bias held by the annotators can also lead to defective annotations @xcite , which is, however, rarely studied. in addition, studies in social science @xcite suggest that people from different demographic groups tend to apply different standards to evaluate the same thing due to their different experiences, which causes group bias. we observe that annotators in different demographic groups tend to show different bias in annotation tasks. for example, in a preliminary study, we examine the instances annotated by both two groups of annotators in the wikipedia toxicity dataset @xcite . we observe that native speakers of english rate 5.1% more comments as toxic than non-native speakers. similarly, annotators over 30 years old rate 2.5% more comments as toxic than younger annotators. more details of the preliminary study can be found in section 2. thus, a thorough investigation of such annotator group bias is desired. similar to existing studies, by considering the effect of annotator group bias, we have the potential to achieve a more accurate inference of true labels and train a better model. meanwhile, it is often hard to estimate the individual bias of one annotator with limited annotation data. with annotator group bias as the prior knowledge, we can estimate the bias more effectively based on the demographic groups the annotator belongs to. thus, annotator group bias could mitigate the "cold-start" problem in modeling the annotator individual bias. in this paper, we aim to study how to detect annotator group bias under text classification tasks, and how to mitigate the detrimental effects of annotator group bias on model training. we face several challenges. first, given noisy annotated data without the true labels, how should we detect the annotator bias? we first make a comparison of the annotation results from different groups of annotators and find that there is a significant gap between them. then, we use two metrics sensitivity and specificity to measure the annotator bias, and conduct an analysis of variance (anova) which demonstrates that the bias of each individual annotator shows obvious group effects in terms of its demographic attributes. second, how can we estimate the annotator group bias, and perform label aggregation and model training with the knowledge of annotator group bias? following the traditional probabilistic approaches for label aggregation @xcite @xcite , we propose a novel framework groupanno that models the production of annotations as a stochastic process via a novel probabilistic graphical model (pgm). inspired by the results of anova, we assume that the bias of an annotator can be viewed as a superposition of the effects of annotator group bias and its individual bias. we thereby extend the original pgm for label aggregation with additional variables representing annotator group bias. by learning the pgm, we estimate the annotator group bias, infer the true labels, and optimize our classification model simultaneously. third, how can we learn this pgm effectively? with the unknown true label as the latent variable, typical maximum likelihood estimation (mle) method cannot be directly applied to estimate the parameters. to address this challenge, we propose an extended em algorithm for groupanno to effectively learn all the parameters in it, including the parameters of the classifier and the newly introduced variables for modeling annotator group bias. we summarize our contributions in this paper as follows. first, we propose metrics to measure the annotator group bias and verify its existence in real nlp datasets via an empirical study. second, we propose a novel framework groupanno to model the annotation process by considering the annotator group bias. third, we propose a novel extended em algorithm for groupanno where we estimate the annotator group bias, infer the true labels, and optimize the text classification model simultaneously. finally, we conduct experiments on synthetic and real data. the experimental results show that groupanno can accurately estimate the annotator group bias. also, compared with competitive baselines, groupanno can infer the true label more accurately, and learn better classification models. ## understanding annotator group bias in this section, we perform an empirical study to get a rudimentary understanding of annotator group bias. ## modeling annotator group bias in this section, we discuss our approaches for annotator group bias estimation, as well as bias-aware label aggregation and model training. we first introduce the metrics for measuring annotator group bias, and then present the problem statement. next, we detail groupanno, the probabilistic graphical model for modeling the production of annotations. finally, we describe our extended em algorithm for learning the proposed model. ## experiment in this section, we evaluate the proposed method via comprehensive experiments. we test our model on both synthetic and real-world data. through the experiments, we try to answer three research questions: (1) is our method able to accurately estimate the annotator group bias? (2) can our method effectively infer the true labels? and (3) can our approach learn more accurate classifiers? ## related work bias and fairness issues are crucial as machine learning systems are being increasingly used in sensitive applications @xcite . bias is caused due to pre-existing societal norms @xcite , data source, data labeling, training algorithms, and postprocessing models. data source bias emerges when the source distribution differs from the target distribution where the model will be applied @xcite . training algorithms can also introduce bias. for example, if we train a model on data that contain labels from two populations -a majority and a minority population -minimizing overall error will fit only the majority population ignoring the minority @xcite . data labeling bias exists when the distribution of the dependent variable in the data source diverges from the ideal distribution @xcite . many of these data labels are generated by human annotators, who can easily skew the distribution of training data @xcite . various factors such as task difficulty, task ambiguity, amount of contextual information made available, and the expertise of the annotator determine annotation results @xcite . prior literature studies various approaches to ensure the reliability of data annotations. @xcite ; @xcite use worker probability to model the ability of an annotator to correctly answer a task, and some other works @xcite introduce a similar concept, worker quality, by changing the value range from [0, 1] to (-∞, +∞). welinder et al. (2010) model the bias and variance of the crowdsourcing workers on numeric annotation tasks. moreover, @xcite and @xcite find that annotators show different qualities when answering different tasks, and thereby propose to model the diverse skills of annotators on various tasks. @xcite realize that annotators perform unevenly on each annotation instance, so they propose a novel method to model the instancelevel annotator reliability for nlp labeling tasks. @xcite use language generated by annotators to identify annotator identity and showed that annotator identity information improves model performance. all these studies have been individualfocused and ignore group effects. our approach differs in that we study systemic bias associated with annotators of a specific demographic group. ## conclusion in this work, we investigate the annotator group bias in crowdsourcing. we first conduct an empirical study on real-world crowdsourcing datasets and show that annotators from the same demographic groups tend to show similar bias in the annotation tasks. we develop a novel framework groupanno that considers the group effect of annotator bias, to model the whole annotation process. to solve the optimization problem of the proposed framework, we propose a novel extended em algorithm. finally, we empirically verify our approach on two synthetic datasets and four real-world datasets. the experimental results show that our model can accurately estimate the annotator group bias, achieve more accurate truth inference, and also train better classifiers that outperform those learned under state-of-the-art true label inference baselines. as future work, we plan to investigate the annotator group bias in tasks beyond classification such as regression tasks and text generation tasks.