# A Survey on Bias in Deep NLP
## 4 Discussion
### Association Tests
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Year** & **Ref.** & **Sterotyped(s)** & **Model** & **Data** & **Lmg.** & **Evaluation** & **Mitigation** & **Stage** & **Task** \\ \hline
2020 [46] & Gender & RoBERTB/Glow (1) & Common Crawul (1) & English & **WEAT’, SIRT** & Vector Spono Manipulation, **OSCaR** & Train & - \\ \hline
2020 [53] & Gender, & BERT, CPT-2, ROBERTs, & **SteroSet** & English & **CAT** Context Association & - & - & Language Modeling \\  & & Religion & XJNet & & & Test & & \\ \hline
2020 [54] & Intersectional & GloVe, EMG, GPT, CPT-2, & Common-Eavul, Bilke Word & English & WEAT, **CEAT** & - & - & - \\  & Bias (Sender, & BERT & English Wikipedia dumps, & & & & \\  & & BoelCappus, WebTel, & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\ \hline
2020 [55] & Gender & Word2 Vec & Wikipedia ees 2006 & Spanish & Analogies & - & - & - \\
2020 [56] & Gender & CBOW & British Library Digital corpus, & English & Association, Prediction & - & - & - \\  & & The Guardian articles & & likelihood, Sentiment & & & \\  & & & & & Analysis & & \\ \hline
2020 [57] & Gender & BERT & GAP, **REC-Pro**, Occupation & English, & Association Test (like & Fine-tuning, CDS & Train & - \\  & & Data (BLS), & German & WEAT) & & & & \\ \hline
2018 [58] & Gender & Deep Coer. [59] & **WineGender**, Occupation & English & Prediction Accuracy & - & - & - &
\begin{tabular}{} \end{tabular} \\ \hline
2018 [53] & Gender & GloVe [27], **CN-Give** & 2017 English/Wijipedia & English & Prediction Accuracy, & Attribute Protection, Vector & Train (1), & Conference \\  & & **Hard-GloVe** & **Hard-GloVe** & dump, **SemBlas (3)** & & Analogies (3) &
\begin{tabular}{} \end{tabular} \\ \hline
2018 [60] & Gender & \
\begin{tabular}{} \end{tabular} & CoNLL-2012, Wikitest-2 & English & Conference score (1), & Data Augmentation (CDA), & Before, &
\begin{tabular}{} \end{tabular} \\  & deep-coref [62] & deep-coref [62] & deep-coref [62] & & likelihood (2) & & & \\ \hline
2019 [63] & Gender & ELMo, GloVe & One Billion Word Benchmark, & English & PCA, Prediction Accuracy &
\begin{tabular}{} \end{tabular} \\  & & & Attribute Protection (gender & After (2) & Resolution \\  & & & swapping averaging (2) & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Previous work on bias detection and treatment in NLP: measuring association (Part 2/2). \begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline
**Year** & **Ref.** & **Sterotyped(s)** & **Model** & **Data** & **Lang.** & **Evaluation** & **Mitigation** & **Stage** & **Task** \\ \hline
2020 [14] & Gender, Race, Religion & **GPT-3** & Common Craw, WebText2, Books2, Wikipedia & English & Text generation & - & - & - \\
2020 [64] & Ideological, Political, Race & GPT-3 & Common Craw, WebText2, Books2, Wikepedia & English & QA, Text Generation & - & - & - \\ \hline
2020 [65] & Race & GPT-3 & Common Craw, WebText2, Books2, Wikepedia & English & Text Generation & - & - & Question Answering \\ \hline
2020 [66] & Gender & Google Translate & United Nations [42], European [43], Google & English, Hungarian & Prediction accuracy - & - & Translation \\  & & & & & & & \\  & & & & & & & \\ \hline
2021 [16] & Ethnicity & GPT-3 & Common Craw, WebText2, Books2, Wikepedia, Humans of New York images & English & Analogies, associations, Text Generation & Positive Contextualization & After & - \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._