# A Survey on Bias in Deep NLP
## 3 Overview on Bias Related Research  
An exhaustive review of relevant papers on bias in natural language processing has been carried out. In order to provide a global view into the different studies and analysis found regarding bias detection and correction, a set of elements have been identified to characterize major issues over all the works compiled. This allows for a organization of up-to-date research work on the targeted matter. These elements are now introduced for better understanding of the following tables as dimensions over the different main aspects in bias related research. The studies are grouped according to their contributions in order to show the progress in the main lines of work. Each line constitutes one of the tables in this section. * **Year**. This column is the publication year in ascending order and will serve as timeline on research progress. It also serves to highlight the increase of interest in the research community over time. We can see that it was not until two years after [26] when the community began to actively work on the bias of word embeddings models. * **Reference** points to the publication. * **Domain(s)** show us in which category falls the studied bias. The most represented category is gender bias, usually showing difference treatment between male and female. The second most represented one is ethnicity bias, in this category we grouped bias against race, ethnicity, nationality or language. We also found work on bias related with age, religion, sexual orientation and disability. It is worth mentioning that there is some work done on political bias. * **Model** will refer to the neural network model studied in the paper. When the bias is not a model but an application, we will refer to such an application. Bias is not only studied in open system but also in black box applications like Google Translate. It is \begin{table}
\begin{tabular}{c l l l} \hline \hline
**Protected Property (\(p_{p}\))** & **Stereotyped Properties (\(P_{s}\))** & **Protected Terms (\(T_{p}\))** & **Stereotyped Terms (\(T_{s}\))** \\ \hline has-gender & \{has-job\} & \{girl, women, & \{doctor, nurse\} \\  & & Christine, man\} & \\ \hline has-age & \{responsibility, & \{\(t\in L\): t \(<\) 25\}, \{\(t\in L\): & \{responsible, efficient, \\  & efficiency\} & t \(>\) 25\} & irresponsible, \\  & & inefficient\} \\ \hline has-religion & \{confidence, & \{Muslim, Christian, & \{terrorist, dangerous, \\  & crime-committed\} & atheist\} & robbery, homicide\} \\ \hline has-profession & \{social-skills, & \{IT specialist, & \{empathetic, friendly, \\  & intelligence, aspect\} & physicist, cleaning & kind, confident, \\  & & lady, politician, & handsome, strong, \\  & & athlete, lawyer, CEO, & intelligent, attractive, \\  & & teacher\} & powerful, influencer\} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simple examples on stereotype formalization. interesting how some studies are able to discover and measure bias in those system. Although they are not able to mitigate the bias directly, there are some samples that manage to reduce the bias without having access to the model by modifying strategically the input. * **Data** will serve as a summary of what data were used. We will consider almost all the resources that have taken part in the study regarding: the information used to train the models to the corpus on which the models is applied, or the other dataset that helps to contextualize the technique used. * **Language** column mainly shows that most of the work has been done on English datasets and models. Some approaches when working with bias in other languages usually have English as a reference point, involving the translation of the data or test sets from English to other languages with both automated tools and paid professionals. Another approach involves looking for analogies between different languages. * column shows the reader which was the technique for evaluation or for measuring the bias:
* **Association tests**.