This paper presents novel experiments shedding light on the shortcomings of
current metrics for assessing biases of gender discrimination made by machine
learning algorithms on textual data. We focus on the Bios dataset, and our
learning task is to predict the occupation of individuals, based on their
biography. Such prediction tasks are common in commercial Natural Language
Processing (NLP) applications such as automatic job recommendations. We address
an important limitation of theoretical discussions dealing with group-wise
fairness metrics: they focus on large datasets, although the norm in many
industrial NLP applications is to use small to reasonably large linguistic
datasets for which the main practical constraint is to get a good prediction
accuracy. We then question how reliable are different popular measures of bias
when the size of the training set is simply sufficient to learn reasonably
accurate predictions. Our experiments sample the Bios dataset and learn more
than 200 models on different sample sizes. This allows us to statistically
study our results and to confirm that common gender bias indices provide
diverging and sometimes unreliable results when applied to relatively small
training and test samples. This highlights the crucial importance of variance
calculations for providing sound results in this field.

论文研究了当前用于评估机器学习算法在文本数据上进行性别歧视的不足之处的度量方法，着重于 Bios 数据集上的职业预测任务，研究表明常用的性别偏差指数在训练集样本较小时具有不可靠性。

公平度量分数是否足以评估机器学习中的歧视偏见？

Are fairness metric scores enough to assess discrimination biases in  machine learning?

Gender discrimination in hiring is a pertinent and persistent bias in
society, and a common motivating example for exploring bias in NLP. However,
the manifestation of gendered language in application materials has received
limited attention. This paper investigates the framing of skills and background
in CVs of self-identified men and women. We introduce a data set of 1.8K
authentic, English-language, CVs from the US, covering 16 occupations, allowing
us to partially control for the confound occupation-specific gender base rates.
We find that (1) women use more verbs evoking impressions of low power; and (2)
classifiers capture gender signal even after data balancing and removal of
pronouns and named entities, and this holds for both transformer-based and
linear classifiers.

通过对 1.8K 个美国男女简历的分析，本文发现虽然分类器用于移除代词和实体，进行数据平衡，但是仍能够捕捉到性别信号，并且女性在简历中使用更多强调低权力的动词，这种性别歧视存在于 CVs 中。

专业陈述和预示的能量：英文简历中隐含性别信息的案例研究

Professional Presentation and Projected Power: A Case Study of Implicit Gender Information in English CVs

Women are influential online, especially in image-based social media such as
Twitter and Instagram. However, many in the network environment contain gender
discrimination and aggressive information, which magnify gender stereotypes and
gender inequality. Therefore, the filtering of illegal content such as gender
discrimination is essential to maintain a healthy social network environment.
In this paper, we describe the system developed by our team for SemEval-2022
Task 5: Multimedia Automatic Misogyny Identification. More specifically, we
introduce two novel system to analyze these posts: a multimodal multi-task
learning architecture that combines Bertweet for text encoding with ResNet-18
for image representation, and a single-flow transformer structure which
combines text embeddings from BERT-Embeddings and image embeddings from several
different modules such as EfficientNet and ResNet. In this manner, we show that
the information behind them can be properly revealed. Our approach achieves
good performance on each of the two subtasks of the current competition,
ranking 15th for Subtask A (0.746 macro F1-score), 11th for Subtask B (0.706
macro F1-score) while exceeding the official baseline results by high margins.

本文描述了我们团队在 SemEval-2022 任务 5 中开发的自动鄙视女性言论辨识系统，该系统采用了 Bertweet 和 ResNet-18 等多种模型进行文本和图像分析，取得了较好的分类效果，排名 15 和 11 位。