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  "evidence": ["In our experiments, we compare the performance of our model against BERT and RoBERTa."]
}

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Paper: Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems

Automatic machine learning systems can inadvertently accentuate and perpetuate inappropriate human biases. Past work on examining inappropriate biases has largely focused on just individual systems. Further, there is no benchmark dataset for examining inappropriate biases in systems. Here for the first time, we present the Equity Evaluation Corpus (EEC), which consists of 8,640 English sentences carefully chosen to tease out biases towards certain races and genders. We use the dataset to examine 219 automatic sentiment analysis systems that took part in a recent shared task, SemEval-2018 Task 1 'Affect in Tweets'. We find that several of the systems show statistically significant bias; that is, they consistently provide slightly higher sentiment intensity predictions for one race or one gender. We make the EEC freely available.

Gender Bias Results
Individual submission results were communicated to the participants. Here, we present the summary results across all the teams. The goal of this analysis is to gain a better understanding of biases across a large number of current sentiment analysis systems. Thus, we partition the submissions into three groups according to the bias they show:
F=M not significant: submissions that showed no statistically significant difference in intensity scores predicted for corresponding female and male noun phrase sentences,
F INLINEFORM0 –M INLINEFORM1 significant: submissions that consistently gave higher scores for sentences with female noun phrases than for corresponding sentences with male noun phrases,
F INLINEFORM0 –M INLINEFORM1 significant: submissions that consistently gave lower scores for sentences with female noun phrases than for corresponding sentences with male noun phrases.
For each system and each sentence pair, we calculate the score difference INLINEFORM0 as the score for the female noun phrase sentence minus the score for the corresponding male noun phrase sentence. Table TABREF24 presents the summary results for each of the bias groups. It has the following columns:
#Subm.: number of submissions in each group.
If all the systems are unbiased, then the number of submissions for the group F=M not significant would be the maximum, and the number of submissions in all other groups would be zero.
Avg. score difference F INLINEFORM0 –M INLINEFORM1 : the average INLINEFORM2 for only those pairs where the score for the female noun phrase sentence is higher. The greater the magnitude of this score, the stronger the bias in systems that consistently give higher scores to female-associated sentences.
Avg. score difference F INLINEFORM0 –M INLINEFORM1 : the average INLINEFORM2 for only those pairs where the score for the female noun phrase sentence is lower. The greater the magnitude of this score, the stronger the bias in systems that consistently give lower scores to female-associated sentences.
Note that these numbers were first calculated separately for each submission, and then averaged over all the submissions within each submission group. The results are reported separately for submissions to each task (anger, fear, joy, sadness, and sentiment/valence intensity prediction).
Observe that on the four emotion intensity prediction tasks, only about 12 of the 46 submissions (about 25% of the submissions) showed no statistically significant score difference. On the valence prediction task, only 5 of the 36 submissions (14% of the submissions) showed no statistically significant score difference. Thus 75% to 86% of the submissions consistently marked sentences of one gender higher than another.
When predicting anger, joy, or valence, the number of systems consistently giving higher scores to sentences with female noun phrases (21–25) is markedly higher than the number of systems giving higher scores to sentences with male noun phrases (8–13). (Recall that higher valence means more positive sentiment.) In contrast, on the fear task, most submissions tended to assign higher scores to sentences with male noun phrases (23) as compared to the number of systems giving higher scores to sentences with female noun phrases (12). When predicting sadness, the number of submissions that mostly assigned higher scores to sentences with female noun phrases (18) is close to the number of submissions that mostly assigned higher scores to sentences with male noun phrases (16). These results are in line with some common stereotypes, such as females are more emotional, and situations involving male agents are more fearful BIBREF27 .
Figure FIGREF25 shows the score differences ( INLINEFORM0 ) for individual systems on the valence regression task. Plots for the four emotion intensity prediction tasks are shown in Figure FIGREF31 in the Appendix. Each point ( ▲, ▼, ●) on the plot corresponds to the difference in scores predicted by the system on one sentence pair. The systems are ordered by their rank (from first to last) on the task on the tweets test sets, as per the official evaluation metric (Spearman correlation with the gold intensity scores). We will refer to the difference between the maximal value of INLINEFORM1 and the minimal value of INLINEFORM2 for a particular system as the INLINEFORM3 –spread. Observe that the INLINEFORM4 –spreads for many systems are rather large, up to 0.57. Depending on the task, the top 10 or top 15 systems as well as some of the worst performing systems tend to have smaller INLINEFORM5 –spreads while the systems with medium to low performance show greater sensitivity to the gender-associated words. Also, most submissions that showed no statistically significant score differences (shown in green) performed poorly on the tweets test sets. Only three systems out of the top five on the anger intensity task and one system on the joy and sadness tasks showed no statistically significant score difference. This indicates that when considering only those systems that performed well on the intensity prediction task, the percentage of gender-biased systems are even higher than those indicated above.
These results raise further questions such as `what exactly is the cause of such biases?' and `why is the bias impacted by the emotion task under consideration?'. Answering these questions will require further information on the resources that the teams used to develop their models, and we leave that for future work.
Average score differences: For submissions that showed statistically significant score differences, the average score difference F INLINEFORM0 –M INLINEFORM1 and the average score difference F INLINEFORM2 –M INLINEFORM3 were INLINEFORM4 . Since the intensity scores range from 0 to 1, 0.03 is 3% of the full range. The maximal score difference ( INLINEFORM5 ) across all the submissions was as high as 0.34. Note, however, that these INLINEFORM6 s are the result of changing just one word in a sentence. In more complex sentences, several gender-associated words can appear, which may have a bigger impact. Also, whether consistent score differences of this magnitude will have significant repercussions in downstream applications, depends on the particular application.
Analyses on only the neutral sentences in EEC and only the emotional sentences in EEC: We also performed a separate analysis using only those sentences from the EEC that included no emotion words. Recall that there are four templates that contain no emotion words. Tables TABREF26 shows these results. We observe similar trends as in the analysis on the full set. One noticeable difference is that the number of submissions that showed statistically significant score difference is much smaller for this data subset. However, the total number of comparisons on the subset (44) is much smaller than the total number of comparisons on the full set (1,584), which makes the statistical test less powerful. Note also that the average score differences on the subset (columns 3 and 4 in Table TABREF26 ) tend to be higher than the differences on the full set (columns 3 and 4 in Table TABREF24 ). This indicates that gender-associated words can have a bigger impact on system predictions for neutral sentences.
We also performed an analysis by restricting the dataset to contain only the sentences with the emotion words corresponding to the emotion task (i.e., submissions to the anger intensity prediction task were evaluated only on sentences with anger words). The results (not shown here) were similar to the results on the full set.

Race Bias Results
We did a similar analysis for race as we did for gender. For each submission on each task, we calculated the difference between the average predicted score on the set of sentences with African American (AA) names and the average predicted score on the set of sentences with European American (EA) names. Then, we aggregated the results over all such sentence pairs in the EEC.
Table TABREF29 shows the results. The table has the same form and structure as the gender result tables. Observe that the number of submissions with no statistically significant score difference for sentences pertaining to the two races is about 5–11 (about 11% to 24%) for the four emotions and 3 (about 8%) for valence. These numbers are even lower than what was found for gender.
The majority of the systems assigned higher scores to sentences with African American names on the tasks of anger, fear, and sadness intensity prediction. On the joy and valence tasks, most submissions tended to assign higher scores to sentences with European American names. These tendencies reflect some common stereotypes that associate African Americans with more negative emotions BIBREF28 .
Figure FIGREF28 shows the score differences for individual systems on race sentence pairs on the valence regression task. Plots for the four emotion intensity prediction tasks are shown in Figure FIGREF32 in the Appendix. Here, the INLINEFORM0 –spreads are smaller than on the gender sentence pairs—from 0 to 0.15. As in the gender analysis, on the valence task the top 13 systems as well as some of the worst performing systems have smaller INLINEFORM1 –spread while the systems with medium to low performance show greater sensitivity to the race-associated names. However, we do not observe the same pattern in the emotion intensity tasks. Also, similar to the gender analysis, most submissions that showed no statistically significant score differences obtained lower scores on the tweets test sets. Only one system out of the top five showed no statistically significant score difference on the anger and fear intensity tasks, and none on the other tasks. Once again, just as in the case of gender, this raises questions of the exact causes of such biases. We hope to explore this in future work.

Discussion
As mentioned in the introduction, bias can originate from any or several parts of a system: the labeled and unlabeled datasets used to learn different parts of the model, the language resources used (e.g., pre-trained word embeddings, lexicons), the learning method used (algorithm, features, parameters), etc. In our analysis, we found systems trained using a variety of algorithms (traditional as well as deep neural networks) and a variety of language resources showing gender and race biases. Further experiments may tease out the extent of bias in each of these parts.
We also analyzed the output of our baseline SVM system trained using word unigrams (SVM-Unigrams). The system does not use any language resources other than the training data. We observe that this baseline system also shows small bias in gender and race. The INLINEFORM0 -spreads for this system were quite small: 0.09 to 0.2 on the gender sentence pairs and less than 0.002 on the race sentence pairs. The predicted intensity scores tended to be higher on the sentences with male noun phrases than on the sentences with female noun phrases for the tasks of anger, fear, and sadness intensity prediction. This tendency was reversed on the task of valence prediction. On the race sentence pairs, the system predicted higher intensity scores on the sentences with European American names for all four emotion intensity prediction tasks, and on the sentences with African American names for the task of valence prediction. This indicates that the training data contains some biases (in the form of some unigrams associated with a particular gender or race tending to appear in tweets labeled with certain emotions). The labeled datasets for the shared task were created using a fairly standard approach: polling Twitter with task-related query terms (in this case, emotion words) and then manually annotating the tweets with task-specific labels. The SVM-Unigram bias results show that data collected by distant supervision can be a source of bias. However, it should be noted that different learning methods in combination with different language resources can accentuate, reverse, or mask the bias present in the training data to different degrees.

Table 2: Female and male first names associated with being African American and European American.

Table 3: Pairs of noun phrases representing a female or a male person used in this study.

Table 4: Emotion words used in this study.

Question: Which race and gender are given higher sentiment intensity predictions?
{"answer": [" the number of systems consistently giving higher scores to sentences with female noun phrases", "higher scores to sentences with African American names on the tasks of anger, fear, and sadness", " joy and valence tasks, most submissions tended to assign higher scores to sentences with European American names"], "evidence": ["When predicting anger, joy, or valence, the number of systems consistently giving higher scores to sentences with female noun phrases (21\u201325) is markedly higher than the number of systems giving higher scores to sentences with male noun phrases (8\u201313). (Recall that higher valence means more positive sentiment.) In contrast, on the fear task, most submissions tended to assign higher scores to sentences with male noun phrases (23) as compared to the number of systems giving higher scores to sentences with female noun phrases (12). When predicting sadness, the number of submissions that mostly assigned higher scores to sentences with female noun phrases (18) is close to the number of submissions that mostly assigned higher scores to sentences with male noun phrases (16). These results are in line with some common stereotypes, such as females are more emotional, and situations involving male agents are more fearful BIBREF27 .", "The majority of the systems assigned higher scores to sentences with African American names on the tasks of anger, fear, and sadness intensity prediction. On the joy and valence tasks, most submissions tended to assign higher scores to sentences with European American names. These tendencies reflect some common stereotypes that associate African Americans with more negative emotions BIBREF28 ."]}