Using Item Response Theory to Measure Gender and Racial Bias of a BERT -based Automated E nglish Speech Assessment System (2022)

Abstract: Recent advances in natural language processing and transformer-based models have made it easier to implement accurate, automated English speech assessments. Yet, without careful examination, applications of these models may exacerbate social prejudices based on gender and race. This study addresses the need to examine potential biases of transformer-based models in the context of automated English speech assessment. For this purpose, we developed a BERT-based automated speech assessment system and investigated gender and racial bias of examinees’ automated scores. Gender and racial bias was measured by examining differential item functioning (DIF) using an item response theory framework. Preliminary results, which focused on a single verbal-response item, showed no statistically significant DIF based on gender or race for automated scores.

## introduction automated speech assessment systems have become prominent at the k-12 and post-secondary levels @xcite . scores produced by automated systems are used for high stakes decisions, such as allocating public funds and determining university admissions decisions. compared to human raters, automated assessments may be more efficient and affordable @xcite , and they may improve reliability @xcite ). yet automated assessments have a unique set of challenges @xcite , and it is important that test developers and researchers continue to improve the overall enterprise of automated speech assessment. researchers have recently begun applying transformer-based models @xcite to english speech assessment. largely, these research efforts have been directed towards improving the accuracy of automated scoring systems. for instance, @xcite has conducted research on bert-based methods in automated essay scoring. in english speech assessment, @xcite compared the performance of bert and xlnet for the purpose of scoring examinees' transcribed responses. results have demonstrated that transformer-based models are highly accurate and correlate strongly with human ratings. although transformer-based models can produce accurate scores, less attention has been devoted to examining the biases of these models. in the field of english speech assessment, no such analyses have been conducted to date. in the broader field of natural language processing (nlp), research has demonstrated that transformer-based models can propagate and, in some cases, exacerbate gender and racial prejudice (e.g. @xcite . biased scoring models certainly have the potential to cause allocational harms @xcite , underscoring the importance of conducting detailed analysis prior to implementation. beyond text modeling, there are additional sources of potential bias in audio processing. audio speech recognition (asr), in particular, may be less accurate for certain language-minority groups (e.g. @xcite . less accurate transcripts, in turn, could lead to biased scores. there are multiple ways to measure bias, and the most appropriate method varies depending on the specific research context. most techniques, however, are similar in that they deteremine the extent to which language modeling outputs-whether word embeddings (e.g. @xcite or inferences (e.g. @xcite )-conform to prostereotype expectations. this study takes a similar overall approach, but is unique in using measurement tools from educational assessment. this study examines a type of bias known as differential item function (dif), which is defined as the systematic difference (in scores) between a reference group and a focal (minority) group, while controlling for overall proficiency @xcite . although bias and fairness are conceptually distinct in educational testing, detection of dif may provide evidence for a larger claim about fairness for certain groups of examinees @xcite . although analysis of dif is common in educational assessment, it has not been applied to studies of bias in nlp. in order to detect dif, we use the improved wald test, which is rooted in item response theory (irt) @xcite . there are a variety of methods used to detect dif but, in general, irt tends to offer the most statistical power @xcite . the improved wald test, in particular, has gained widespread adoption because it is sensitive to small group differences, holding constant examinees' overall proficiency @xcite . the research design of this study involves three principal components: (1) constructing an asr system, (2) training a transformer-based scoring model, and (3) investigating potential gender and racial bias based on these automated scores. our analyses focus on a single speaking item. although we found no statistically significant result in the automated scores for this item, analyses will soon be expanded to a larger pool of items and multiple grade bands which may be more susceptible to automated scoring bias. ## methods below, we describe the key methodological aspects of the research project. these include (1) the source of data used in analyses, (2) the design and development of our automated english speech assessment system, and (3) the statistical techniques used to measure gender and racial bias. ## results two wald tests were conducted for each dif comparison: one to test the significance of the discrimination parameter, a, and the other to test the significance of the difficulty parameters, b. b is written in bold to indicate that it is a vector of difficulty parameters. there are two degrees of freedom for tests of differences of b, corresponding to the two difficulty parameters. observed p-values were calculated based on χ 2 and df . critical p-values were determined a-priori using the benjamini-hochberg adjustment. although not shown, p-values would have been significant if any p obs. had been lower than its corresponding p crit. . although not presented here, there were also no significant differences found in human-rated scores, based on gender or race. ## conclusion and next steps transformer-based models have gained widespread attention due to their highly accurate predictions and correlations with human ratings, yet it is important that issues of fairness be addressed concurrently. our study constitutes a step forward in automated english speech assessment by examining bias in bert-based scoring models. our study also demonstrates how item response theory can be used to identify differential item functioning (dif) in the context of automated scoring-a practice that is common in educational assessment, yet uncommon in the field of natural language processing. although our analysis did not find any gender or race dif in automated scores produced by our bert-based model, we refrain from drawing general conclusions about the bias of such models for english speech assessment. in this instance, we found no evidence of bias, yet it is possible that such biases are more prominent in lengthier speaking items, for older groups of examinees, or for different language minorities. indeed, based on research of implicit bias @xcite , we might expect more bias in lengthier items or for older students. the next step of our research project is to take up these challenges by expanding dif analyses to different types of speaking items, multiple age groups, and respondents with different home-languages. in addition to expanding the scope of the current analysis, next steps also include experimenting with a wider variety of transformer-based models and asr systems. incorporating audio data into the scoring model, for instance, may improve accuracy yet also change the behavior of the automated scoring system. if biases are detected, then there will be further opportunities to explore sources of bias and to apply debiasing techniques that have been developed for other applications of transformer-based models @xcite .