A Section Wise Summary of the paper by Thomas Davidson, Debasmita Bhattacharya and Ingmar Weber published in 2019 at the Third Abusive Language Workshop at the Annual Meeting for the Association for Computational Linguistics 2019.

Abstract

• The authors essentially recognized the inherent bias in the types of dataset mentioned in titles.
• When dealing with tweets with African American English they are much more likely to be recognized as hateful over American English tweets.
• This bias can be detrimental as then the trained models will flag an African American User much more just because of the English they use when the sentiment might not be as negative as the model thinks.
• This means this may flag the victims of abuse instead of protecting them

Introduction

• Some works have shown bias in trained models which are caused due to bias in the data the model trains on.
• Bias may reduce the accuracy rendering the model unable to detect abuse and in the worst case might even discriminate against the very people it was built to protect.
• The paper studies models trained on Standard American English (SAE) and African American English (AAE) datasets all of which use data scraped from Twitter.
• The authors used Bootstrap Sampling (Bootstrapping is any test or metric that uses random sampling with replacement (e.g. mimicking the sampling process), and falls under the broader class of resampling methods) to estimate how many tweets fall in each class according to the model.
• Bias was observed in all classes with AAE tweets being flagged more than SAE tweets.
• Keeping out certain words does reduce the bias significantly however it still persists and is present in the trained model.

Related Works

• Bias often causes wrong predictions as seen with facial recognition technology. They perform far worse for darker-skinned women as compared to a white male face. This is due to the lack of data for the former when compared to the latter.
• Research also shows in unsupervised learning the embeddings learnt also contain biases which persist even after removal. Youtube captioning system also works far worse for women. Many classifiers even classify AAE as non english.
• Although it is difficult to find a perfect model but still we should be concerned if the model has inherent bias against a group by the virtue of the data it was trained on. Annotation Scheme and Annotator Identity manipulation also helps kin reducing bias.
• Google's Perspective API Classifier is an example which was trained on Wikipedia talk comments and poorly classifies even a simple sentence referring to homosexuals in a normal fashion as toxic since a majority of data which it was trained on was biased and saw those words in the wrong light.
• Even Hate speech detectors work very poorly when female identity terms are present.

Research Design

Hate Speech & Abusive Language Datasets

The paper focuses on Twitter Datasets which is widely used for these purposes

Waseem and Hory 2016

• 130k tweets containing one of 17 hateful terms. Annotated by themselves
• Reviewed by a 25 year old women studying gender studies and a nonactivist feminist.
• 16,849 tweets labelled either racist, sexist or neither
• To account for bias Waseem relabeled 2876 tweets
• Annotated by feminist and anti-racism activists
• Fourth category of racism and sexism was added
• Dataset has about 6909 tweets

Davidson et al. (2017)

• Collected tweets having words from the HateBase.
• To avoid bias crowd workers were instructed not to straight away label something based on word phrases rather use the overall tweet and inferred context.
• 24,783 annotated tweets are present which are classified as either hate speech, offensive language or neither.

Golbeck et al. (2017)

• Tweets were selected using ten keywords and phrases related to anti-black racism, Islamophobia, homophobia, anti-Semitism, and sexism.
• Initial coding scheme involved multiple classes such as threats or hate speech.
• Finally the authors went with a binary approach of harassment or non-harassment label.
• 20,360 Handlabelled tweets are present.

Founta et al. (2018)

• Randomly chose tweets having words from the HateBase lexicon. They criticized old works where labels were added in an ad hoc fashion
• They first allowed each tweet to lie in multiple classes.
• They then analyzed overlaps between classes and ended up with 4 classes abusive, hateful, spam and normal.
• Their database of 91,951 tweets is used in this paper.

Training Classifiers

• A classifier is trained for each dataset
• Logistic Regression is used along with bag-of-word features. In bag-of-word model, a text is represented as the bag of its words, disregarding grammar and even word order but keeping multiplicity.
• While using more complex models might be better but recall the problem was with our dataset and biases will inevitably creep in.
• Embeddings are not used as some research has shown bias in pretrained embeddings.
• Each tweet is preprocessed by removing URLS and Mentions and replacing them with placeholders.
• We form a TF-IDF matrix out of each dataset allowing a maximum of 10000 features. TFIDF, short for term frequency–inverse document frequency, is a numerical statistic that is intended to reflect how important a word is to a document in a collection or corpus.
• 80% of the dataset is used for training while rest is used for validation.
• Model is trained using Stratified 5-Fold Cross Validation.
• Grid search is performed over different regularization set parameters to find best performing model.
• For each dataset we find model with best F1 score and we train it using all data.
• Their performance on the 20% held out validation set is recorded in Table 1.
• It was noticed hate speech and harassment is difficult to detect which is understandable since it can be easily confused as offensive text.

Race Dataset

• We use a tweet dataset labelled by race to measure racial biases in the classifier.
• The tweets were geolocated and then the different ethnic proportions in that area were retrieved.
• The model was trained to learn the different language styles of each ethnicity.
• We then get the posterior proportion of language of each ethnic group in each tweet.
• Their validation analyses indicate that tweets with a high posterior proportion of non-Hispanic black language exhibit lexical, phonological, and syntactic variation consistent with prior research on AAE.
• The dataset had about 59.2 Million Tweets
• They defined a user as likely non-Hispanic black if the average posterior proportion across all of their tweets for the non-Hispanic black language model is >= 0.80 (and <= 0.10 Hispanic and Asian combined) and as non-Hispanic white using the same formula but for the white language model.
• For memory reasons the authors only focused on a set of 1.1 Million tweets written by people who generally use non-Hispanic black language and 14.5m tweets written by users who tend to use non-Hispanic white language.
• These datasets are then referred to as black-aligned and white-aligned which reflects that this particular set consists a language style associated with a particular group.

Experiments

• Essentially we go back to our original mission, is there a bias? To check that we see if a particular tweet being predicted to be something has anything to do with it's racial alignment. So we fall back on our trusty null hypothesis toolkit, here the paper does a very good job of explaining it itself with all the proper equations as can be seen below -

• To explain this a bit although I highly encourage you to read it yourself the null hypothesis is written mathematically and we explore the two possibilities of it being bending on either side and it's implications respectively being either white-aligned tweets are classified more often or either the black-aligned ones are.
• We then test the hypothesis using bootstrap sampling to find out how many tweets are belonging to a particular class according to the model.
• The mathematical details can be seen in the paper for the same but essentially we compare the proportion, do a t-test and other statistical techniques are used.
• Another experiment brought up two results. Certain racial slurs and sexist words were much more prevalent in some sets, hence unbalancing the dataset itself. Since these words are linked to negative sentiments they skew our dataset and hence introduce bias inevitably.

Results

• We found large statistical distinctions between the two racial alignments meaning that black-aligned tweets are much more frequently classified to be negative over white-aligned ones. In only one case there was a minor difference with white-aligned ones being worse off however even there black-aligned ones are more likely to be classified as sexist.

The result table below shows it evidently -

Discussion -

• There was a consistent, systematic bias in observations in all datasets hinting to a huge overall problem with our models when deployed online.
• Certain phrases associated and used more by a certain ethnic group skews the dataset and hence adds bias. The second experiment also ascertained that even if we use tweets which have the keywords still one side is more often classified as negative.
• Racism prediction was not very effective and a probable cause is that the data labelled racist was more anti-muslim in nature than anti-black.
• The crux of the problem is AAE tweets are much more likely to use certain words which our model learns to classify as negative very quickly and doesn't update it's beliefs.
• Over different datasets very interesting observations are well explained in the paper and deserve a read.

Conclusion

• Evidence of substantial racial bias is found
• Bias remains even when comparing tweets with certain key words
• Caution is needed when using this model for real world deployment as it is prone to mistakes and misclassifications
• Apart from extra penalizing African Americans the authors also suspect other ethnic groups might also be at risk
• The problem arises as we use keywords in building those datasets which is not very representative of the data itself
• In the HateBase lexicon also AAE is over represented which might cause this as they were oversampled due to some keywords
• Another potential cause is bias brought by annotators and the paper shows some data to back this claim as well
• The annotator workplace is also a point to question since they might've not been very English proficient in some cases being in Venezuela
• Over different datasets the classified proportion varied hugely from 1% to 18% suggesting lack of generalization on training data
• This brings a point of knowing how a particular abuse is used in some culture, where it might not be as hateful as it may be to some other.
• The 'n-word' is a case in itself as it may vary between polar opposite meanings depending on context and it's slight modifications are representative of white supremacy mentality while some are so common that you'll find people referring to each other using it in the ethnic group.
• So considering the word itself hateful biases our data as it is a widely used word in AAE.
• This brings us back to the root of everything, context, context matters and we need to find a way to be able to leverage that. Without context brute-forchish methods are bound to be lacking in some way.

Limitations

• Since the dataset lacked racial info. about the tweet authors we might very well have people who tweet in both AAE and SAE.
• The second experiment though looks pretty alluring may not be a result of bias and rather the words which are used to classify tweets as abusive or not might just be the words which are used to predict the race, making it not an issue of bias rather a problem of different negative classes in different datasets. Some bias might be removed with more advanced modelling techniques
• We'll also need to look at what keywords did our classifier learn to look for in negative samples and that might give us more insights
• This work was limited to racial bias however with further work we can look for other biases as well