Comparing Intrinsic Gender Bias Evaluation Measures without using Human Annotated Examples (2023)

Abstract: Numerous types of social biases have been identified in pre-trained language models (PLMs), and various intrinsic bias evaluation measures have been proposed for quantifying those social biases. Prior works have relied on human annotated examples to compare existing intrinsic bias evaluation measures. However, this approach is not easily adaptable to different languages nor amenable to large scale evaluations due to the costs and difficulties when recruiting human annotators. To overcome this limitation, we propose a method to compare intrinsic gender bias evaluation measures without relying on human-annotated examples. Specifically, we create multiple bias-controlled versions of PLMs using varying amounts of male vs. female gendered sentences, mined automatically from an unannotated corpus using gender-related word lists. Next, each bias-controlled PLM is evaluated using an intrinsic bias evaluation measure, and the rank correlation between the computed bias scores and the gender proportions used to fine-tune the PLMs is computed. Experiments on multiple corpora and PLMs repeatedly show that the correlations reported by our proposed method that does not require human annotated examples are comparable to those computed using human annotated examples in prior work.

## introduction pre-trained language models (plms) trained on large datasets have reported impressive performance improvements in various nlp tasks @xcite greatly. however, these plms also demonstrate significantly worrying levels of social biases @xcite . to address this issue, numerous intrinsic bias evaluation measures for plms have * danushka bollegala holds concurrent appointments as a professor at university of liverpool and as an amazon scholar. this paper describes work performed at the university of liverpool and is not associated with amazon. figure 1 : overview of our proposed method. we first create bias-controlled plms by fine-tuning a plm on male and female gendered sentences that are automatically mined from unannotated corpora. next, we measure the rank correlation between the scores reported by an intrinsic bias evaluation measure and the male/female bias rates (r) used to fine-tune the plms. existing bias evaluation methods use different criteria such as pseudo likelihood @xcite , cosine similarity @xcite , inner-product @xcite etc. moreover, current bias evaluation methods require manually-annotated datasets containing stereotypical and antistereotypical examples that express different types of social biases @xcite . therefore, we consider that it is important to compare the differences in existing bias evaluation measures proposed for plms @xcite @xcite to understand their relative strengths and weaknesses. to objectively compare the existing bias evaluation measures, @xcite calculated the rank correlation between the number of human annotators who labelled an example to be stereotypically biased towards a protected attribute in crowds-pairs (cp), and the bias score for that example returned by an intrinsic bias evaluation measure @xcite . however, due to the costs and difficulties in recruiting human annotators, this approach cannot be easily adapted to different languages, accommodate large-scale evaluations, or compare evaluation metrics that do not use human-annotated data. we propose a method to compare intrinsic bias evaluation measures without using human annotated examples. figure 1 outlines the intuition behind our proposed method. first, we train biascontrolled versions of plms obtained via finetuning a plm on male and female gendered sentences, automatically mined from an unannotated corpus using a gender-related word list. we define rate of bias (r) as the ratio between male and female gendered sentences in a training sample used to fine-tune a plm. a plm fine-tuned mostly on male sentences is likely to generate sentences containing mostly male words, while a plm finetuned on female sentences is likely to generate sentences containing mostly female words @xcite . therefore, an accurate intrinsic bias evaluation measure is expected to return a score indicating a bias towards the male gender for a male bias-controlled plm, while it is expected to return a score indicating a bias towards the female gender for a female bias-controlled plm. we then compute the rank correlation between (a) the rate of biases in the bias-controlled plms, and (b) the bias scores returned by an intrinsic evaluation measure for the corresponding plms, as a measure of accuracy of the bias evaluation measure. our experiments with multiple corpora and plms show that the correlations reported by our proposed method, which does not require human annotated examples, are comparable to those computed using human annotated examples in previous studies. furthermore, by examining the output probabilities of the plm, we verify that the proposed method, which fine-tunes bias-controlled plms with varying amounts of male vs. female sentences, is indeed able to control biases associated with male and female gender directions. ## bias-controlled fine-tuning the imbalance of gender words in the training data affects the gender bias of a plm fine-tuned using that data @xcite . using this fact, we propose a method to learn bias-controlled versions of plms that express different levels of known gender biases. let us first assume that we are given a list of female gender related words v f (e.g. she, woman, female), and a separate list of male gender related words v m (e.g. he, man, male). next, we select sentences that contain either at least one of female or male words from an unannotated set of sentences d. sentences that contain both male and female words are excluded here. let us denote the set of sentences extracted for a female or a male word w by φ(w). moreover, let d @xmath0. we appropriately downsample d f and d m to have equal numbers of sentences next, we create training datasets d r by varying the rate of bias, r (∈ [0, 1]), by randomly sampling a subset s r (d m ) of n r sentences from d f and a subset when @xmath1, d r consists of only female sentences (i.e. d r ⊆ d f ), and when @xmath2, it consists of only male sentences (i.e. d r ⊆ d m ). to obtain multiple bias-controlled plms at different levels of gender biases, we fine-tune a given plm on different datasets, d r , sampled with different values of r. we use a given intrinsic bias evaluation measure to separately evaluate each bias-controlled plm. finally, we measure the agreement between the bias scores reported by the intrinsic bias evaluation measure under consideration and the corresponding rates of biases of those plms using pearson's rank correlation coefficient. ## conclusion we proposed a method to compare intrinsic gender bias evaluation measures using an unannotated corpus and gender-related word lists. experiments show that the correlations computed by the proposed method for existing bias evaluation measures agrees with the prior evaluations conducted using human annotations.