Hate Speech takes many forms to target communities with derogatory comments,
and takes humanity a step back in societal progress. HateXplain is a recently
published and first dataset to use annotated spans in the form of rationales,
along with speech classification categories and targeted communities to make
the classification more humanlike, explainable, accurate and less biased. We
tune BERT to perform this task in the form of rationales and class prediction,
and compare our performance on different metrics spanning across accuracy,
explainability and bias. Our novelty is threefold. Firstly, we experiment with
the amalgamated rationale class loss with different importance values.
Secondly, we experiment extensively with the ground truth attention values for
the rationales. With the introduction of conservative and lenient attentions,
we compare performance of the model on HateXplain and test our hypothesis.
Thirdly, in order to improve the unintended bias in our models, we use masking
of the target community words and note the improvement in bias and
explainability metrics. Overall, we are successful in achieving model
explanability, bias removal and several incremental improvements on the
original BERT implementation.

HateXplain 采用注释的句子片段、言论分类和针对性群体，使分类更像人类，更易于解释、更准确、更少偏见。我们将 BERT 调整为使用 rationales 和类别预测进行此任务，并比较了我们在准确性、可解释性和偏见方面的不同指标上的表现。我们的新颖之处在于三个方面，首先，我们使用不同重要性值的合并 rationale 类损失进行实验。其次，我们在 rationales 上广泛实验了 ground truth attention 值。第三，为了提高我们的模型中的无意偏见，我们使用了目标社区单词的屏蔽，并记录了偏见和可解释性指标的改善。总的来说，我们成功地实现了模型的可解释性、偏见消除，并在原始 BERT 实现上取得了几项增量改进。

利用 HateXplain 和 BERT 探索仇恨言论检测

Exploring Hate Speech Detection with HateXplain and BERT

Feature attribution methods, proposed recently, help users interpret the
predictions of complex models. Our approach integrates feature attributions
into the objective function to allow machine learning practitioners to
incorporate priors in model building. To demonstrate the effectiveness our
technique, we apply it to two tasks: (1) mitigating unintended bias in text
classifiers by neutralizing identity terms; (2) improving classifier
performance in a scarce data setting by forcing the model to focus on toxic
terms. Our approach adds an L2 distance loss between feature attributions and
task-specific prior values to the objective. Our experiments show that i) a
classifier trained with our technique reduces undesired model biases without a
trade off on the original task; ii) incorporating priors helps model
performance in scarce data settings.

该论文提出了一种将特征规定融入机器学习模型建立的目标函数的方法，以解决文本分类中的偏见和数据稀缺性问题，并通过实验证明其有效性。

基于特征归因和先验知识的文本分类方法

Incorporating Priors with Feature Attribution on Text Classification

Unintended bias in Machine Learning can manifest as systemic differences in
performance for different demographic groups, potentially compounding existing
challenges to fairness in society at large. In this paper, we introduce a suite
of threshold-agnostic metrics that provide a nuanced view of this unintended
bias, by considering the various ways that a classifier's score distribution
can vary across designated groups. We also introduce a large new test set of
online comments with crowd-sourced annotations for identity references. We use
this to show how our metrics can be used to find new and potentially subtle
unintended bias in existing public models.

该论文通过引入一套阈值不可知的度量标准，考虑分类器分数分布在指定群体中的变化，从而提供了深入了解机器学习中的未预期偏差的视角，该方法可帮助发现现有公共模型中的新的潜在微妙的偏差，并使用在线评论和众包注释展示该度量标准的运用。

用实际数据衡量文本分类的意外偏误的微妙度量

Nuanced Metrics for Measuring Unintended Bias with Real Data for Text  Classification

Recent work has raised concerns on the risk of unintended bias in AI systems
being used nowadays that can affect individuals unfairly based on race, gender
or religion, among other possible characteristics. While a lot of bias metrics
and fairness definitions have been proposed in recent years, there is no
consensus on which metric/definition should be used and there are very few
available resources to operationalize them. Therefore, despite recent
awareness, auditing for bias and fairness when developing and deploying AI
systems is not yet a standard practice. We present Aequitas, an open source
bias and fairness audit toolkit that is an intuitive and easy to use addition
to the machine learning workflow, enabling users to seamlessly test models for
several bias and fairness metrics in relation to multiple population
sub-groups. Aequitas facilitates informed and equitable decisions around
developing and deploying algorithmic decision making systems for both data
scientists, machine learning researchers and policymakers.

研究人员开发了一个名为 Aequitas 的开源工具包，该工具包可帮助数据科学家、机器学习研究人员和政策制定者测试多个偏差和公平度量标准，以实现对算法决策制定系统进行有根据的和公平的决策。