A Comprehensive View of the Biases of Toxicity and Sentiment Analysis
  Methods Towards Utterances with African American English Expressions
Guilherme H. Resende Luiz F. Nery Fabrício Benevenuto Savvas Zannettou Flavio Figueiredo
Language is a dynamic aspect of our culture that changes when expressed in
different technologies/communities. Online social networks have enabled the
diffusion and evolution of different dialects, including African American
English (AAE). However, this increased usage is not without barriers. One
particular barrier is how sentiment (Vader, TextBlob, and Flair) and toxicity
(Google's Perspective and the open-source Detoxify) methods present biases
towards utterances with AAE expressions. Consider Google's Perspective to
understand bias. Here, an utterance such as ``All n*ggers deserve to die
respectfully. The police murder us.'' it reaches a higher toxicity than
``African-Americans deserve to die respectfully. The police murder us.''. This
score difference likely arises because the tool cannot understand the
re-appropriation of the term ``n*gger''. One explanation for this bias is that
AI models are trained on limited datasets, and using such a term in training
data is more likely to appear in a toxic utterance. While this may be
plausible, the tool will make mistakes regardless. Here, we study bias on two
Web-based (YouTube and Twitter) datasets and two spoken English datasets. Our
analysis shows how most models present biases towards AAE in most settings. We
isolate the impact of AAE expression usage via linguistic control features from
the Linguistic Inquiry and Word Count (LIWC) software, grammatical control
features extracted via Part-of-Speech (PoS) tagging from Natural Language
Processing (NLP) models, and the semantic of utterances by comparing sentence
embeddings from recent language models. We present consistent results on how a
heavy usage of AAE expressions may cause the speaker to be considered
substantially more toxic, even when speaking about nearly the same subject. Our
study complements similar analyses focusing on small datasets and/or one method
only.