Racial Bias in Hate Speech and Abusive Language Detection Datasets (1905.12516v1)

Abstract: Technologies for abusive language detection are being developed and applied with little consideration of their potential biases. We examine racial bias in five different sets of Twitter data annotated for hate speech and abusive language. We train classifiers on these datasets and compare the predictions of these classifiers on tweets written in African-American English with those written in Standard American English. The results show evidence of systematic racial bias in all datasets, as classifiers trained on them tend to predict that tweets written in African-American English are abusive at substantially higher rates. If these abusive language detection systems are used in the field they will therefore have a disproportionate negative impact on African-American social media users. Consequently, these systems may discriminate against the groups who are often the targets of the abuse we are trying to detect.

Racial Bias in Hate Speech and Abusive Language Detection Datasets: An Expert Overview

The paper "Racial Bias in Hate Speech and Abusive Language Detection Datasets" by Davidson, Bhattacharya, and Weber critically evaluates the systematic biases present in machine learning classifiers tasked with identifying hate speech and abusive language on social media platforms, specifically Twitter. This paper explores the potential racial bias inherent in five well-known datasets containing annotated abusive language, extending this important discussion in the field of NLP.

Core Investigations and Methodology

The authors focus their analysis on whether tweets written in African-American English (AAE) are disproportionately classified as abusive when compared to tweets written in Standard American English (SAE). The datasets evaluated vary in size and annotation methods, comprising examples of tweets labeled for offensive, abusive, or hateful content. The classifiers themselves are based on regularized logistic regression models with bag-of-words features and are evaluated for bias using a corpus of demographic-tagged tweets.

Key aspects of the research design include:

• Dataset Selection: Evaluation across datasets by Waseem (2016), Davidson et al. (2017), Golbeck et al. (2017), Founta et al. (2018), and Waseem and Hovy (2016).
• Corpus and Classifier Training: Training classifiers on available datasets and assessing their performance on AAE versus SAE.
• Experiments: Employing bootstrap sampling to gauge bias by comparing predicted class membership proportions between "black-aligned" and "white-aligned" tweets, including conditioned analysis on typical negative-content keywords.

Results and Analysis

From the analysis, there exists a discernible and statistically significant bias in classifier performance with tweets in black-aligned corpora being more frequently and incorrectly classified into negative classes compared to white-aligned corpora. This trend persists even when controlling for the presence of certain keywords, indicating that seemingly innocuous features of AAE are being incorrectly associated with hate speech or abuse.

Key Findings:

• Classifier Disparities: Classifiers trained across different datasets exhibit varying levels of bias, with certain datasets showing pronounced racial disparities.
• Impact of Keywords: Despite conditioning on terms like “n*gga” and “b*tch”, black-aligned tweets continue to be inaccurately flagged as abusive more often than white-aligned tweets.
• Classification Challenges: Specific classes like hate speech and offensive language showcased higher misclassification rates, particularly impacting AAE dialects.

Theoretical and Practical Implications

The paper substantiates concerns about racial bias in NLP tools used for content moderation on social media platforms and raises critical questions about the fairness and ethical deployment of such systems in operational settings. Specifically, the paper posits that:

• The deployment of biased classifiers could exacerbate racial discrimination, penalizing demographic groups that are already marginalized.
• Abusive language detection systems require refinement to avoid culturally insensitive biases and must be sensitive to linguistic variances between racial and ethnic communities.

Future Directions

This work necessitates concerted efforts to rectify bias at the data collection and annotation stages, emphasizing the need for representative sampling and nuanced contextual analyses to ensure equitable model performance. Key areas for further research include:

• Developing annotation frameworks that minimize individual and systemic bias from annotators.
• Creating datasets that better reflect the diversity and nuance of language use across different demographic groups.
• Exploring alternative modeling approaches, such as contextual embeddings, that might more accurately grasp the subtleties of AAE in relation to abusive language detection.

The paper is a significant contribution, highlighting the complexities involved in building equitable technology and calling for enhanced transparency and fairness in AI systems, pivotal for more ethical and effective deployment in real-world applications.