Global Voices, Local Biases: Socio-Cultural Prejudices across Languages
Authors: Anjishnu Mukherjee
Chahat Raj
Ziwei Zhu
Antonios Anastasopoulos
Venue: EMNLP 2023 Main (2023)
Keywords: bias
multilinguality
language models

Abstract: Human biases are ubiquitous but not uniform: disparities exist across linguistic, cultural, and societal borders. As large amounts of recent literature suggest, language models (LMs) trained on human data can reflect and often amplify the effects of these social biases. However, the vast majority of existing studies on bias are heavily skewed towards Western and European languages. 
In this work, we scale the Word Embedding Association Test (WEAT) to 24 languages, enabling broader studies and yielding interesting findings about LM bias. We additionally enhance this data with culturally relevant information for each language, capturing local contexts on a global scale. 
Further, to encompass more widely prevalent societal biases, we examine new bias dimensions across toxicity, ableism, and more. 
Moreover, we delve deeper into the Indian linguistic landscape, conducting a comprehensive regional bias analysis across six prevalent Indian languages. 
Finally, we highlight the significance of these social biases and the new dimensions through an extensive comparison of embedding methods, reinforcing the need to address them in pursuit of more equitable language models.

Global Voices, Local Biases: Socio-Culturee Prejudices across Languages
Anjishnu Mukherjee' Chahat Raj" Ziwei Zhu Antonios Anastasopoulos
Department of Computer Science, George Mason University (amukher6, craj, zzhu20, antonis Jegmu.
Abstract
literature. First, the vast majority of the efforts on bias identification and mitigation in language repre- sentations, starting with the Word Embedding As- sociation Test (WEAT; Caliskan et al., 2017), have been solely conducted on English. Some recent studies have also delved into other languages like Arabic, French, Spanish, and German, primarily by adapting WEAT to the languages (Lauscher and Glavaš, 2019) and integrating limited cultural con- texts for non-social human biases (España-Bonel and Barrón-Cedeñoo, 2022), but still remain cen- tered on the global North. Second, even though NLP has largely moved towards contextualized rep- resentations from transformer models like BERT, most works for determining bias in word embed- dings have predominantly studied static encoding methods like FastText (Joulin al., 2016) and GLoVe (Caliskan al., 2022). To address these challenges, our work makes 5 contributions:
Human biases are ubiquitous but not uniform: disparities exist across linguistic, cultural, and societal borders. As large amounts of recent lit- erature suggest, language models (LMs) trained human data reflect and often amplify the effects of these social biases, However, the vast majority of existing studies on bias are heav- ily skewed towards Westemn and European lan- guages. In this work, we scale the Word Emhed- ding Association Test (WEAT) 24 languages, enabling broader studies and yielding interest- ing findings about LM bias. We additionally enhance this data with culturally relevant in- formation for each language, capturing local contexts on a global scale. Further, to encom- pass more widely prevalent societal biases, examine new bias dimensions across toxicity, ableism, and more. Moreover, we delve deeper into the Indian linguistic landscape, conducting a comprehensive regional bias analysis across six prevalent Indian languages. Finally, we highlight the significance of these social biases and the new dimensions through an extensive comparison of embedding methods, reinforc- ing the need to address them in pursuit of more equitable language models.!
First, we construct a comprehensive dataset and perform in-depth analyses comparing machine vs. human translation for 24 non-English languages, encompassing previously overlooked ones from In- dia and the global South. Additionally, we incorpo- rate munguage-specifie culturally relevant data wher- ever possible for both human and socio-cultural biases, covering all relevant categories in WEAT, for identifying biases based on the relative proximity of word embeddings in latent space by comparing associations with different classes social biases. The languages in our dataset include Arabic (ar), Bengali (bn), Sorani Kurdish (ckb), Danish (da), German (de). Greek (el), Spanish (es), Persian (fa), French (fr), Hindi (hi), Italian (it).
1 Introduction
Language models, trained on large text corpora, have been shown to reflect and often exacerbate the societal perceptions found therein (Weidinger et al., 2021). As a result, there is a potential of harm (Bender et al., 2021; Kumar al., 2023), e.g., due to the reinforcement of unfair representations of certain groups, especially in downstream usage of the representations from these models.
Japanese (ja), Korean (ko), Kurmanji Kurdish (ku),
Despite the already growing interest in bias iden- tification and mitigation in language representa- tions, we identify several major shortcomings the
Marathi (mr), Punjabi (pa), Russian (ru), Telugu
(te), Thai (th), Tagalog (tI). Turkish (tr), Urdu (ur).
Vietnamese (vi), and Mandarin Chinese (zh). Moving beyond English requires solutions often ignored language-specific challenges. Our study includes the analysis of
"qqual contribution
All code, data and results are available here: https:// gi thub. www. http///ammhowwwaatteeeeee
expres-

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sions corresponding a single English word, ubiq- uitous languages like Vietnamese or Thai. We also reformulate the WEAT metric to account for
WEAT IDs and Target Attribute Pairs
Flowers/Insects (PleasantUnplleasant))
2. Instruments/eeapnns (Pleasant/Unpllaasee
6. Male/Female Names (Career/Family)
translation ambiguities a principled manner. Recognizing the dynamic nature of societal bi- ases, next observe that certain prevalent prej- udices remain unaddressed in the existing WEAT categories. To remedy this, we propose five dimen- sions of human-centered biases that reflect con- temporary ideologies and stereotypes (Mei al., 2023), including toxicity, ableism, sexuality, edu- cation, and immigration.
7. Math/Art (Male/Female Terms)
(cience/Art (Male/Female Terms)
Mental/Physical Disease (Temporary/Permaeen)
Table I: WEATHub for assessing biases across lan- guages using six target-attribute pairs.
these names for different languages is challeng- ing, given that European American versus African American dichotomy is not particularly pertinent in other languages. We also omit WEAT 10, which requires names of older and younger individuals, as it is a vaguely defined category that may not generalize well across languages. Consequently, our multilingual dataset for 24 languages focuses WEAT categories 1, 6, 7, 8, and 9 (Table 1), an approach similar to that employed by Lauscher and Glavaš (2019).
In addition, we go beyond examining static em- beddings and conduct comprchensive analysis comparing various techniques for extracting word embeddings from transformer models. We also in- vestigate the impact of employing a multilingual model (Levy et al., 2023) trained on a corpus of 100+ languages vs. monolingual models to under- stand which approach is better suited for capturing societal biases within different cultural contexts.
To collect annotated data in various languages, we provide our annotators with the English words and their corresponding automatic translation, sep- arated by WEAT category. We provide instructions to verify the accuracy of the translations and pro- vide corrected versions for any inaccuracies. Ad- ditionally, we ask annotators to provide grammat- ically gendered forms of words, if applicable, or multiple translations of a word, if necessary. All annotators who participated in our study are native speakers of their respective languages and have at least college-level education background. We also request that annotators provide five additional language-speciffe words per category to incorpo- rate even more language context in our measure- ments of multilingual bias. For example, our Greek annotator added "anemone," " while our Danish an- notator added "chamomile" and "rose hip" for the WEAT category of flowers.
The final contribution of this work is an in-depth analysis of bias in seven Indian languages. We contrast multilingual and monolingual models and delve into relevant dimensions of social bias in India, inspired by Malik et al. (2022). Our experi- ments investigate gender bias in grammatically gen- dered languages such as Hindi vs. neutral ones like Bengali, alongside examining biases against lower castes, Islam, and professions associated with In- dia's rural populace.
2 Data
To measure bias in a multilingual setting, we in- troduce WEATHub, a dataset that extends relevant WEAT categories to 24 languages, including cul- tural context, where applicable. Further, include five new humann-centere dimensions to capture the multifaceted nature of contemporary biases.
2.1 WEATHub: Multilingual WEAT
2.2 Human-centered Contemporary Biases
Building on the 10 tests for measuring word em- bedding associations between different groups of English words by Caliskan et al. (2017), we create a dataset of target and attribute pairs for evaluating different dimensions of bias in 24 languages.
Bias is as pervasive and varied as the human ex- perience itself. While some biases, such as the positive association with flowers and the negative connotations linked with insects, may be univer- sal, other forms are far more complex and nuanced. Notably, the biases encountered by the LGBTQ+ community, immigrants, those with disabilities are neither simple universally recognized. Drawing upon research on interscctional biases (Tan and Celis, 2019; Hassan et al., 2021; Magee
We exclude tests involving target attribute words specific to European American or African American names, as they lack relevance for non- Western and non- European languages. Further- more, directly creating generalized translations of

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Bias Dimensions
Targets (Attributes)
Toxicity Education Bias
Offensive/Respeecful Words (Female/Male Terms)
Sincatted/ooneduuccaee Terms (Higher Status/Lower Status Words)
Immigration Bias
Aomnigran/Non-immiigen Terms (DisrespecctuuRRespeettu Words)
Ableism-Gender
Insul/Disabillity Words (Femalc/Malc Terms)
Ableism-Valence
Insult/Disability Words (Unpleasan//lleasan Words)
Percetiinn LGBTQ+//trright Words (Prejudice/Pride)
Eexuality-Valenee
LGBTQ+/Straight Words (Unpleasan/Pleesant Words)
Table 2: Five target-attribute pairs in WEATHub for human-ccntere contemporary bias assessment in 24 languages.
et al., 2021; Kirk et al., 2021; Parrish et al., 2022; Elsafoury, 2022: Mei et al.. 2023), we propose five new dimensions Toxicity, Ableism, Sexuality, Ed- ucation, and Immigration bias. These are designed to fill in the gaps by explicitly targeting the assess- ment of social biases currently captured within the existing WEAT dimensions. Table delineates all the targets and attributes that gauge associations between different social groups.
24 languages establishing a foundational consensus on bias dimensions across linguistic communities. (3) Subsequently, we formulate the corresponding target/attribute pairs after weighing different op- tions. (4) Finally, opt for naming convention that felt best depicts these biases.
To establish the vocabulary used to measure asso- ciations, we created a basic set of words in English, building upon initial lexicon proposed by the authors by querying web-based resources and vari- ous open- and closed-souree large language models. We then conversed with our annotators re- garding the relevance of these lexical items across diverse cultural contexts, eventually selecting set of ten words per category that demonstrate the high- est degree of cross-cultural applicability. For the valence measurements, re-used the pleasant and unpleasant words available from original WEAT categories 1 and 2 (Table 1). All of the word lists are available our code repository.
The toxicity bias dimension tests the hypothe- sis that words associated with femininity are more likely to be linked with offensive language than those related to masculinity. Similarly, ableism bias seeks to discern prevalent biases against in- dividuals with disabilities, examining if they are more frequently subject to derogatory language when their disability is mentioned. Meanwhile, the sexuality bias dimension examines the poten- tial biases associated with different sexual orienta- tions, specifically the use of LGBTQ+ and straight (cisgender, heterosexual and other non-LGBTQ++) words. The education bias analyzes educated and non-educated terms against a backdrop of higher and lower-status words to detect societal stereo- types about one's education. Last, the immigration bias dimension assesses whether immigrants are treated equally across different cultural contexts. While these dimensions were decided based on pre- vious research and experiences of selected native speakers of different languages, we agree that fram- ing biases under one general umbrella is difficult, We thus additionally frame the Ableism and Sex- uality dimensions in terms of word associations with pleasantness (Omrani Sabbaghi al., 2023) offer more intuitive angle for the problem.
2.3 India-Specific Bias Dimensions
Malik et al. (2022) provide an interesting case study for assessing bias in Hindi language representa- tions, explicitly relating to various dimensions of bias pertinent to the Indian context. For instance, the caste religion of individual can often be inferred from their surnames, suggesting that as- sociations may exist between a person's 's surname and specific target attributes such as descriptive adjectives or typical occupations. Furthermore, the grammatical gender inherent in Hindi neces- sitates investigating the associations between gen- dered words and male or female terms. We broaden these dimensions of bias to encompass seven di- verse Indian languages and sub-cultures: English, Hindi, Bengali, Telugu, Marathi, Punjabi, and Urdu. Our study facilitates examining whether biases ob- served in Hindi persist other Indian languages.
To summarize our methodology for selecting the proposed dimensions: (1) We start with insights from intersectional biases research. (2) Then we engage in discussions with native speakers of the

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3 Methods
Comparison of different encoding methods
(Higher captures more bias]
0.04
To overcome challenges arising from multilingual- ity, including but not limited to multi-word expres- sions and the absence of one-to-one translations from English to many languages, we reformulate the test statistic for WEAT. We also give heuristic to help choose among multiple embedding methods for contextualized representations. The heuristic is designed to compare embedding methods and is not a bias measurement technique like WEAT or Log Probability Bias Score (Kurita et al., 2019).
Sensitivity (p)
0.03
0.02
0.01
0.00
M1
M2
M3
M4
M5
M6
M7
M8
M9
M10
Figure I: An example of the Korean language shows M; (average of embeddings from all hidden layers and considering average of subwords) the contextualized embedding method with the highest sensitivity, a finding consistent across languages.
3.1 WEAT
The Word Embedding Association Test (WEAT), proposed initially by Caliskan et al. (2017), defines the differential association of a target word ur with the attribute sets A and B as the difference in the means over A and B of the similarities of target word with words in the attribute sets:
A negative effect size indicates an inverse as- sociation between the current target and attribute groups indicating that we reverse the target sets while keeping the attribute groups the same, a pos- itive association would be observed. A one-sided p-value greater than 0.95 accompanying negative effect size implies that the association is statisti- cally significant after switching the targets.
s( sw,, A, B) = Jsice a) Mbe b)],
where sim any similarity metric (here the cosine similarity) between embeddings. The test statistic for a permutation test over target sets X and Y, is then given as
3.2 Bias Sensitivity Evaluation
S(X,Y,A,B)
Σ Tx
s(z,A, B)
WEAT has traditionally been applied to uncover biases static word embeddings like FastText and GloVe (Lauscher and Glavaš, 2019; Caliskan et al., 2022; Sesari et al., 2022). However, recent devel- opments in the field have seen a significant shift towards contextualized embeddings, e.g. using transformer models such BERT (Tan and Celis, 2019; Kurita al., 2019: Silva et al., 2021). Vari- ous methodologies to extract contextualized word embedding representations from BERT exist, as partially explored by Devlin et al. (2019), but no single method consistently excels in representing words for detecting biased associations in WEAT across languages. Table 3 outlines the 10 encoding methods examine.
Σ (yy
We modify the original definition here to calculate the test statistic as below, allowing us to account for and Y of different lengths.
[Mre s A, B)
M A, A, B)]
For the null hypothesis stating that there is no statistically significant difference between the association the two target groups with the two attribute groups, the p-value for the one-sided permutation test can thus be computed using this modified test statistic
In order to capture the extent to which an embed- ding method can discern alterations in word sets, influenced by translations into non-English lan- guages and the inclusion of language -peccfii and gendered terms, introduce a heuristic termed bias 'sensitivity'. This heuristic measures the erage variance of pairwise cosine distances within word sets for each encoding approach. This ratio- nale stems from the workings of the Word Embed- ding Association Test (WEAT), which computes
p = Pr[S B)) S'X,Y,,,
where compute the test statistic over all par- titions of the combined set of target words and check if it strictly larger than the test statistic from the original sets of target words.
We calculate Cohen's 's effect size d as
S' S'(XYY,A,B) O sww A, B)

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Encoding methods
Comparisan effect
Fost Roct
Macléie
Tansiation
Thnis1tor
bert layer subword avg (Mi)/first (M2)
bert 2nd last layer subword avg (Ma))firs((Ma)
bert all layers subword avg (M5)/first (Ma)
Weote
Wenry
Weatd
72%
33
bert last layer CLS (M17)
bert last layer subword avg (Mg)/fist (M₂)
(
(b)
Text (M10)
Figure Ms (average of embeddings from all hidden
layers and considering average of subwords) and M10 (FastText) exhibit higher effect sizes for Human Trans- lations than Machine Translations across languages, as demonstrated for Korean.
Table 3:
Cross-lingual analysis reveals M,, Mg. M1 and Mio as the most sensitive for evaluating bias among the examined encoding methods.
pairwise cosine distances between groups of words, and posits that an encoding method exhibiting a higher variance in these distances would be more 'sensitive' to variations in these word sets. Formally, Bias Sensitivity (pi) for method Mj:
et al., 2020) for extracting contextualized word embeddings
(Conneau al., 2020) and DistilmBERT (Sanh et al., 2020). We employ our hcuristic of bias sensitivity p to guide our analysis. Our results² are compared with those from monolingual BERT models when available for a particular language for a broader perspective on language-specific biases. Additionally, we delve into Indian languages more extensively using an IndicBERT model (Kakwani et al., 2020).
6
= Σ
V
Σ f=1
Viske
6
k=1
Here, Vijk is the variance of pairwise cosine dis- tances for each set in each WEAT category, j and k denote the WEAT category and set index, re- spectively. The sensitivity is the mean of average variances across the WEAT categories.
3.3 The need for human translations
Our study compares machine translations (MT) provided by Google Translate with human transla- tions (HT). To do so, we consider the same gender for grammatically gendered words and exclude the Lunguage-speciff information our dataset. This experiment aims to answer whether human transla- tions are necessary to identify biases across differ- ent cultures or if MT systems are sufficient. Nev- ertheless, our findings show that for any given WEAT category,
Our findings indicate that the embedding layer from BERT, providing static word representations, exhibits the highest sensitivity, closely followed by FastText embeddings. However, as our analysis is centered on contextualized word embeddings, we select the representations from taking the mean of embeddings obtained from all hidden layers of BERT, considering the average of the subwords in each token (method Ms from Figure 1), as this ap- proach displays the subsequent highest sensitivity among the methods we chose for analysis. Re- sults for the other three most sensitive methods are available in the Appendix A, B, C, and D. The CLS- based approach (M7) is the only method where we consider the context of all the subwords and all the tokens in a phrase to get single embedding from a contextual model like BERT without any subword averaging hidden layer selections involved, but it is observed that this method usually has very low sensitivity scores indicating that it may not be suitable to use for WEAT effect size calculations.
human translations more frequently yield larger effect sizes than MT, sug- gesting that sole reliance on MT may not suffice for bias evaluation across languages.
data produces a larger abso- lute effect size for all six categories in Korean. We also compare two of our most sensitive encoding methods in Figure 2 static FastText and embed- dings averaged across all hidden layers of BERT and find that regardless of encoding method or lan- guage, human translations provide more compre- hensive evaluation of bias, yielding larger absolute effect sizes than MT for the same category. MT systems are rarely perfect and may not understand the context in which a word is being used.
Hluman-translaae
Experimental Setting We analyze cross- linguistic patterns for both existing WEAT categories and our newly introduced human- centered dimensions using two widely used multilingual models from Hugging Face (Wolf
Another important example is from German, where gender bias has significant results when us-
2 Data and Results are Available here : https: //gi thub. com/iamshnoo/ /wethuu

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ing Google Translate but when we use all of the language-speciff as well as gendered words along with the human translations, the results become sta- tistically insignificant. Therefore, we recommend utilizing human-annotated data for an accurate and fair assessment of bias across languages instead of solely relying on machine translation systems.
languages like Chinese and Telugu frequently demonstrate positive and significant biases across multiple categories. On the other hand, languages like Urdu, Tagalog, and Russian typically exhibit minimal positive or negative biases. Languages that lie in between, like Arabic, also make for inter- esting case studies because of some extreme effect sizes for particular WEAT categories, pointing to potential unique influences causing these outliers.
4 Results
We perform various experiments to understand how biases vary across languages, models, and embedding methods. In this section, we present the key takeaways from our experiments by high- lighting statistically significant results, including some anecdotal examples from native speakers of a language aligned with our empirical findings in Appendix F. While we acknowledge that these ex- amples might be influenced by the individual ex- periences of the selected annotator, it is crucial to understand that they are presented only as illustra- tive supplements to our statistically substantiated conclusions, not as a singular, potentially biased human validation of our results.
Discussion Inconsistencies across languages derscore the need for further exploration into the role of contextual understanding in the manifesta- tion of biases. These cross-linguistic patterns in WEAT provide insights into the intricate dynamics of bias and emphasize the need for further nuanced investigation to comprehend the origin of these biases. We note, however, that these biases repre- sent the findings of our experiments on querying language models in different languages and might be due the biases (or lack thereof) expressed in those languages in the training corpora. Still, the training corpora for a given language must have originated from human-writ wrrtten text at some point, implying that its biases might directly reflect the language-level (or community-leve) biases.
We also cover a case study of biases within the Indian context to explore common biased associ- ations in India. The figures represent results for our chosen embedding method M5 (mean of em- beddings from all hidden layers considering the average of subwords in each token) as discussed in section 3.2. Results from the other embedding methods are available in the Appendix.
4.2 Multilingual vs Monolingual models
Models like DistilmBERT, which are trained on diverse range of 104 languages, compared to monolingual models that focus on specific lan- guage corpora, result in different findings about bias. Both types of models come with advantages and disadvantages. The selection between the two should be based on the individual requirements and context of the research or application.
4.1 Cross-linguistic patterns in WEAT
Our exploration of bias across languages and en- coding methods reveals intriguing variations for different WEAT categories (Figure 3).
Culturally aligned biases can be discovered in monolingual models, sometimes in contrast to conclusions from multilingual model, de- spite both results being statistically significant. Take, for instance, the Thai context for WEAT 1, which relates flowers and insects to pleasantness and unpleasantness, respectively. Here, the mul- tilingual model confirms the traditional associa- tion of flowers being pleasant and insects being unpleasant. Interestingly, Thai-specific monolin- gual model robustly and statistically significantly contradicted this assumption (Appendix Table F.5). A web search shows multiple references claiming that Thais consider certain insects as culinary deli- cacies, indicating a preference for them. To verify this, we consulted with our Thai human annota-
Contextualized embeddings often encode bi- ases differently than static representations like FastText or the embedding layer in transform- ers. For instance, when encoding methods involv- ing BERT hidden layers are used, languages like Chinese and Japanese consistently display pro- nounced positive bias. However, the same lan- guages exhibit mixed or negative biases when using FastText or BERT's embedding layer. Urdu and Kurdish also follow a similar pattern, often reveal- ing strong negative biases, though some categories and methods indicate low positive biases.
Some languages consistently have positive bi- ases while others have extreme effect sizes for specific categories. For example, high-resource

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WEAT 1 Flowers insedts (Picasaniness)
WEAT 2: Instruments vs Weapans (Picasantness)
WEAT 6: Male vs Female Names (Career vs Family)
WEAT 7: Math vs Art (Gender terms)
WEAT 8 Science vs Art (Gender Terms)
WEAT 9: Mental vs Physical Disease (Duration)
bn
80
Figure 3: Effect size d across languages for Ms (average embeddings from hidden layers and considering average of subwords) in DistilmBERT. Significant results at 95% level of confidence colored and shaded. Negative values of d indicate reversed associations.
tor, who views eating insects as a common activity without explicitly associating it with liking or dis- liking. Therefore, in this particular instance, favor the findings of the monolingual model.
monalingcal
bm
hi
WLOLS
weatZ
Weat1 WeaT
Weat1
Biases are sometimes found in monolingual models even when these are not deemed statis- tically significant when querying multilingual model. For instance, the WEAT 7 category, which associates maths with male terms and arts with female terms, shows negative yet statistically in- significant effect sizes for Tagalog using multiple embedding methods multilingual model. How- ever, when monolingual model used, these negative associations not only grow effect size but also achieve statistical significance (Appendix Table F.6). Furthermore, for Turkish, the mono- lingual model identifies sigmificant positive bias for WEAT and WEAT 2 (associating instruments with pleasantness and weapons with unpleasant- ness), which the multilingual model does not de- tect. On discussing with our Tagalog annotator, they agreed on some aspects of WEAT showing female bias they tend to associate math words more with females and art words with males, hence our findings perhaps indeed do reflect true social perceptions among Tagalog speakers.
Weit2
shwitt
Weik1
Figure 4: Monolingual models generally have larger effect sizes across languages and WEAT categories for Ms (average of embeddings from all hidden layers and considering average of subwords). Markers (circles. diamonds, and squares) show significant results.
flect biases identically to monolingual models for each such language.This observation is evi- dent from our case study involving six Indian lan- guages. Figure 43 contrasts DistilmBERT with IndicBERT model, pre-trained on twelve major In- dian languages and monolingual models designed for each of the languages under consideration.
For Bengali, there is a high consistency across all models and WEAT categories. In languages like Hindi and Marathi, monolingual models exhibit the most significant effect sizes for WEAT 6 and 9, while DistilmBERT presents comparable perfor- mance with monolingual models for the remaining categories. Interestingly, the DistilmBERT model exhibits proficiency in lower-resouree languages like Punjabi, uncovering larger effect sizes across most WEAT categories, except for WEAT 9. Simi- larly, DistilmBERT typically exhibits larger effect sizes for Telugu than the other models. However, the monolingual and IndieBERT models register
For high-resource languages, there is no sig- nificant difference in results from monolingual model and generic multilingual model. For example, in Chinese, substantial and statistically significant biases for WEAT 1, 2, 7, and 8 are ob- served for both multilingual and monolingual mod- els (Appendix Table F.8). We also find significant bias for WEAT 6 for the multilingual model, which is only detectable in the monolingual model for specific embedding methods.
model trained on a group of languages from the same language family does not always re-
Appenndx Figure E,18 provides a higher resolution figure.

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secually-valence
H0.22
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sewually-percepttinn
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0.35
0..73
0.01
5.53
1.00
1.30
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+0.97
5.65
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ableiom-vallnce
0.78
0.79
5.28
0.79
0.92
00.73
education
10.33
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-249
3.39
-.880
2.21
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toxicity
0:46
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Figure 5: Effect sizes from M5 (average of embeddings from hidden layers and considering average of subwords) for contemporary biases in DistilBERTT; significant biases are indicated in bold, evidencing diverse language- specific trends across all dimensions.
larger effect sizes for Urdu than DistilmBERT.
When considering the first subword of each token, there is significant negative bias in the ableism category. However, when using the average of all subwords, significant bias is observed. We addi- tionally measure word associations of ableism with pleasantness measured by valence. Mostly sim- ilar trends are observed with differences some of the languages. An interesting observation is that the effect sizes are sometimes reversed in directions across the two aspects of comparison for ableism. In Russian, for example, strong positive bias is observed towards females but a strong negative association with unpleasant words.
Overall, monolingual models typically manifest effect sizes equal or larger than those obtained from IndicBERT, exhibiting the lowest bias across various languages and WEAT categories. Further- more, DistilmBERT can equal or surpass the effect size of the monolingual model in most languages, excluding Urdu. Multilingual models may not cap- ture some Lungugge-speciffe biases that monolin- gual models can. Conversely, monolingual models yield results that multilingual models might over- look. These findings emphasize the complex role model selection in analyzing linguistic biases.
Considerable bias is observed in language models regarding education and immigration across various languages. Hindi, Italian, Telugu, Chinese, and even English exhibit significant positive bias, associating higher social status with education. However, there are exceptions to this trend. French, Punjabi, and Turkish display sig- nificant negative biases, suggesting that education is not universally linked to higher social status across cultures. Regarding immigration bias, our analysis explores whether terms related to immi- gration are more commonly associated with disre- spectful words than respectful ones used for non- immigrants. A diverse range of languages from different cultures (Spanish, Italian, Kurmanji Kur- dish, Turkish, Korean, Chinese, Bengali, Marathi, Punjabi, Urdu) demonstrate a significant bias for this category across various methods. These find- ings indicate that immigrants more likely to be associated with disrespectful words in the embed- dings from the multilingual DistilmBERT language model. However, English and Hindi exhibit ne- gative biases, suggesting that immigrants are more likely to be respected in those languages.
4.3 Analysis of Human-Centered Biases
Our newly introduced dimensions of bias capture relevant modern social concepts, and we find sta- tistically significant results from WEAT tests on DistilmBERT, HLM-RoBERTA. and also FastText, showing that these biases exist across languages.
Ableism is present in language models, but it varies across languages. We first assess ableism by examining the association of insulting terms with female terminology and the prevalence of disability-related terms with male terminology. Greek an example where this phenomenon is evident most encoding methods. Using vari- ous encoding methods, our experiments provide strong evidence of negative associations in German and Sorani Kurdish. In the case of Arabic, the embedding layer or CLS representation from the last layer of our multilingual DistilmBERT model shows fewer associations between female terms and insults. However, other encoding methods demonstrate a statistically significant positive bias this category. Similar disparities across layers of the same model are also observed in Chinese.

---

Language
Bias type
WEAT
Effect size (p-value)
Bengali
Religion
Adjectives vs Religion terms
-1.179 (0.996)
Gender
Gendered entities vs Male, Female terms
0.824 (0.040)
Hindi
Religion
Adjectives vs Religion terms
0.965 (0.020)
Religion
Adjectives vs Last Names
1.414 (0.001)
Punjabi
Caste
Adjectives vs Caste Names
-1,216 (0.995)
Gender
Stereo Adjectives Male, Female terms
-1.221 (0.994)
Urdu
Caste
Adjectives vs Caste Names
-1.107 (0.993)
Occupation
Adjectives Urban/Rural occupations
0.997 (0.029)
Table Statistically significant results for India-specific bias dimensions religion, caste, occupation and gender from Ms (average of embeddings from all hidden layers and considering average of subwords) of DistilmBERT. Negative effect sizes are significant after reversing targets.
Sexuality bias exists in multiple languages, whereas toxicity bias appears to be less preva- lent. In terms of the perception angle, Spanish, Persian, and Korean demonstrate substantial posi- tive biases across multiple methods in WEAT 13, indicating a prevalent prejudice against LGBTQ+ communities. When using the multilingual Dis- tilmBERT model, most languages do not exhibit significant biases for toxicity. However, upon com- paring with the ALM-RoBERTa model, significant negative biases are predominantly observed, indi- cating that female terms are frequently associated with respectful words, while male terms tend to correlate with offensive ones. Additionally, we also measure associations with pleasantness and this valence angle also has similar trends across languages barring a few like Hindi and Japanese where the effect sizes are reversed in direction.
for example, embeddings for Urdu reverse gender bias by associating male terms with stereotypical female adjectives and vice versa.
The main takeaway from our study is that the stereotypes of bias in Hindi, the most prevalent lan- guage in India, are not equally reflected in other Indian languages. Recent work like Bhatt et al. (2022) provides further guidelines for exploring fairness across Indian languages. Table 4 lists the biases with significant effect sizes from our experi- ments. The complete set of results is available in our code repository.
Conclusion
We introduce multilingual culturally-relevan dataset for evaluating 11 dimensions of intrinsic bias in 24 languages using target and attribute pairs for the WEAT metric. Beyond previously-studiee dimensions of social bias like gender, we propose new dimensions of human-centere contemporary biases like ableism and sexuality to find strong ev- idence of bias in language models. We show that bias does not uniformly manifest across languages, but monolingual models do reflect human biases more closely than multilingual ones across multiple methods of extracting contextualized word embed- dings. We also find that human translations are bet- ter suited for bias studies than automated (machine) translation ones. Finally, our case study on Indian languages reveals that biases in resource-heavy lan- guages like Hindi are not necessarily found in other languages. WEAT, however, is an inconsistent met- ric for measuring biases, as indicated by lim- ited number of statistically significant results. In the future, we aim to develop metries better suited for measuring biases contextualized embeddings and generative models and explore their effects in real-world downstream applications.
Figure highlights these patterns of inherent bi- ases that might exist in the structure of languages themselves or how they are used in different cul- tural contexts. However, it is crucial to note that these observations are based on the biases learned by the language model and may not necessarily re- flect the attitudes of all speakers of these languages.
4.4 Biases in Indian languages
Malik et al. (2022) define some dimensions of bias common India, for example, associating negative adjectives with the Muslim minority or discrimi- nating based on caste. These biases are reflected strongly in Hindi when using word embeddings from a static model like FastText or contextual mod- cls like ElMo (Peters et al., 2018). However, for the other Indian languages we explored, namely En- glish, Bengali, Urdu, Punjabi, Marathi, and Telugu, neither FastText nor embeddings from BERT have statistically significant results for most these bi- ases. However, we find some surprising patterns;

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Limitations
resources for experiments were provided by the Of- fice of Research Computing at George Mason Uni- versity (URL: https: / //rc. //r.ggguuu....../oo.........
For most of the languages in our dataset WEATHub, we had access to at least two anno- tators for cross-verifying the accuracy of the human translations to determine if the trans- lated words fit into the context of that par- ticular WEAT category. However, for some languages, we only have one annotator per language. We plan to make our dataset avail- able via open-source, opening it up to future crowdsourcing possibilities where we would look to get at least two annotators for each language where possible.
g.w.ggu.eee. edu) and funded in part by grants from the National Science Foun- dation (Awards Number 1625039 and 201863) 18631).
References
Pablo Badilla, Felipe Bravo- Marque,, and Jorge Pérez. 2020. Wefe: The word embeddings fairness evalua- tion framework. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelli- gence, JC,AI-20, pages 430 436. International Joint Conferences on Artificial Intelligence Organization. Main track.
While we have tried to cover as many lan- guages from the global South as possible, we acknowledge that 24 languages are indeed a tiny proportion of the 7000 languages in the world, some of which do not even have text representations. Bias detection and mitigation in specch is one direction of research we plan to work on in the future. Another critical step is to facilitate crowdsoureing of WEATHub to cover low resource languages ("The Left- Behinds" and "The Scraping- "rraping-BBss" from the taxonomy introduced in (Joshi et al., 2020) so that we can have fairer systems not just for high resource languages but for all languages.
Emily M Bender, Timnit Gebru, Angelina McMillan- Major, and Shmargaret Shmitchell. 2021. On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM confer- ence on fairness, accountability, and transparency. pages 610-623.
Shaily Bhatt, Sunipa Dev, Partha Talukdar, Shachi Dave, and Vinodkumar Prabhakaran. 2022. Re- contexualizing fairness in NLP: The case of India. In Proceedings of the 2nd Conference of the Asia- Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Confer- ence on Natural Language Processing (Volume 1: Long Papers), pages 727-740.
Aylin Caliskan, Pimparkar Parth Ajay. Tessa Charlesworth, Robert Wolfe, and Mahzarin R Banaji. 2022. Gender bias in word embeddings: comprehensive analysis of frequeney, syntax, and semantics. In Proceedings of the 2022 AAAI/ACM Conference AI, Ethics, and Society, pages 156-170.
Among many other studies, Kurita et al. (2019) has previously shown how WEAT can be an unreliable metric for contextualized em- beddings from transformer models. Silva et al. (2021) furthers this by showing how "debi- asing" based WEAT alone does not truly represent a bias-free model as other metrics still find bias. Badilla al. (2020) provides framework to simultaneously compare and rank embeddings based on different metrics. However, these studies reinforce that we need better metrics to study intrinsic biases in trans- former models. We believe the target and at- tribute pairs we provide as part of WEATHub in multiple languages is an important step to- wards a better multilingual metric for evaluat- ing intrinsic biases in language models.
Aylin Caliskan, Joanna Bryson, and Arvind Narayanan. 2017. Semantics derived automatically from lan- guage corpora contain human-like biases. Science, 356(6334):
Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettle- moyer, and Veselin Stoyanov. 2020. Unsupervised lingull representation learning at scale. In Pro- ceedings of the 58th Annual Meeting of the Asso- ciation for Computational Linguistics, pages 8440- 8451.
Jacob Devlin, Ming-Was Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language under- standing. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Tech- nologies, Volume (Long and Short Papers), pages 4171-4186, Minneapolis, Minnesota. Association for Computational Linguistics.
Acknowledgments
We are grateful to the anonymous reviewers for their constructive feedback. This work was gener- ously supported by by the National Science Foun- dation under award IIS (SS-2327143 143. Computational

---

Fatma Elsafoury. 2022. Darkness can not drive out darkness: Investigating bias in hate speechdetection models. In Proceedings of the 60th Annual Meet- ing of the Association for Computational Linguisties: Student Research Workshop, pages 31-43.
Anne Lauscher and Goran Glavaš. 2019. Are we con- sistently biased? multidimensional analysis of biases in distributional word vectors. In Proceedings of the Eighth Joint Conference on Lexical and Com- putational Semantics (*SEM 2019), pages 85-91, Minneapolis, Minnesota.
Cristina España-Bonis and Alberto Barrón-Cedeñoo. 2022. The (undesired) attenuation of human biases by multilinguality. Proceedings of the 2022 Con- ference on Empirical Methods in Natural Language Processing, Abu Dhabi, United Arab Emirates.
Sharon Levy, Neha Anna John, Ling Liu, Yogarshi Vyas, Jie Ma. Yoshinari Fujinuma, Miguel Ballesteros, Vit- torio Castelli, and Dan Roth. 2023, Comparing bi- ases and the impact of multilingual training across multiple languages. arXiv:2305.1 1242.
Saad Hassan, Matt Huenerfauth, and Cecilia Ovesdotter Alm. 2021. Unpacking the interdependent systems of discrimination: Ableist bias in NLP systems through an intersectional lens. In Findings of the Association for Computational Linguistics: EMNLP 2021. pages 31 5116-3123, Punta Cana, Dominican Republic.
Liam Magee, Lida Ghahremanlou, Karen Soldatic, and Shanthi Robertson. 2021. Intersectional bias in causal language models. urXiv:2107.0766
Vijit Malik, Sunipa Dev, Akihiro Nishi, Nanyun Peng. and Kai-Wei Chang. 2022. Socially aware bias mea- surements for Hindi language representations. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computa- rional Linguistics: Human Language Technologies. pages 1041-1052 Seattle, United States.
Pratik Joshi, Sebastin Santy, Amar Budhiraja, Kalika Bali, and Monojit Choudhury. 2020. The state and fate of linguistic diversity and inclusion in the NLP world. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6282-66 6293, Online. Association for Computational Linguistics.
Katelyn Mei, Sonia Fereidooni, and Aylin Caliskan. 2023. Bias against 93 stigmatized groups in masked language models and downstream sentiment classifi- cation tasks. In Proceedings of the 2023 ACM Confer- ence on Fairness, Accountability, and Transparency, pages 1699-1710.
Armand Joulin, Edouard Grave, Piotr Bojanowski, Matthijs Douze, Hérve Jégou, and Tomas Mikolov. 2016. Fasttext. zip: Compressing text classification models. a
Shiva Omrani Sabbaghi, Robert Wolfe, and Aylin Caliskan. 2023. Evaluating biased attitude associa- tions of language models in an intersectional context. In Proceedings of the 2023 AAAT/ACM Conference AL. Ethics, and Society, AIES '23, page 542 -43,,53, New York, NY, USA. Association for Computing Machinery.
Divyanshu Kakwani, Anoop Kunchukuttan, Satish Golla, NC Gokul, Avik Bhattacharyya, Mitesh M Khapra, and Pratyush Kumar. 2020. Indienipsuite: Monolingual corpora, evaluation benchmarks and pre-trained multilingual language models for indian languages. In Findings of the Association for Com- putational Linguistics: EMNLP 2020, pages 4948- 4961.
Alicia Parrish, Angelica Chen, Nikita Nangia, Vishakh Padmakumar, Jason Phang, Jana Thompson. Phu Mon Huut, and Samuel Bowman. 2022. BBQ: A hand-built bias benchmark for question answering. In Findings of the Association for Computational Linguistics: ACL 2022, pages 2086-2105, Dublin, Ireland. Association for Computational Linguistics.
Hannah Rose Kirk, Yennie Jun, Filippo Volpin, Haider Iqbal. Elias Benussi, Frederic Dreyer, Aleksandar Shtedritski, and Yuki Asano. 2021. Bias out-of-the- box: An empirical analysis of intersectional occupa- tional biases popular generative language models. Advances in neural information processing systems, 34:2611-22
Matthew E. Peters, Mark Neumann, Mohit Iyyer. Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zeulemoyer. 2018. Deep contextualized word repre- sentations. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Humun Lunguage Tech- nologies, Volume I (Long Papers), pages 2227-2237, New Orleans, Louisiana. Association for Computa- tional Linguistics.
Sachin Kumar, Vidhisha Balachandran, Lucille Njoo, Antonios Anastasopoulos, and Yulia Tsvetkov. 2023. Language generation models can cause harm: So what can we do about it? actionable survey. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Lin- guistics. pages 3299-3321, Dubrovnik, Croatia. As- sociation for Computational Linguistics.
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 2020. Distilbert, a distilled ver- sion of bert: smaller, faster, cheaper and lighter. 10X
Keita Kurita, Nidhi Vyas, Ayush Pareek, Alan W Black. and Yulia Tsvetkov. 2019. Quantifying social bi- ases in contextual word representations. In 1st ACL Workshop on Gender Bias for Natural Language Pro- cessing.
Emeralda Sesari, Max Hort, and Federica Sarro. 2022. An empirical study on the faimess of pre-trained

---

word embeddings. Proceedings of the 4th Work- shop on Gender Bias in Natural Language Process- ing (GeBNIP)), pages 129-144.
Andrew Silva, Pradyumna Tambwekar, and Matthew Gombolay. 2021. Towards a comprehensive under- standing and accurate evaluation of societal biases in pre-trained transformers. In Proceedings of the 2021 Conference of the North American Chapter the Association for Computational Linguistics: Human Language Technologies, pages 2383-2389
Yi Chem Tan and L Elisa Celis. 2019. Assessing
cial and intersectional biases in contextualized word representations. Advances in newral information pro- cessing systems, 32.
Laura Weidinger, John Mellor, Maribeth Rauh, Conor Griffin, Jonathan Uesato, Po-Sen Huang, Myra Cheng, Mia Glaese, Borja Balle, Atoosa Kasirzadeh, al. 2021. Ethical and social risks of harm from language models. arXiv:2112.0435.
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pier- ric Cistac, Tim Rault, Remi Louf, Morgan Funtow- icz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma. Yacine Jernite. Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush. 2020. Trans- formers: State-of-the-art natural language processing, In Proceedings the 2020 Conference Empirical Methods in Natural Language Processing: System Demonstrations, pages 38-45, Online. Association for Computational Linguistics.

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A Effect sizes for original WEAT categories from DistilmBERT and Fast
WEAT 1 Flowers vs ingedts (Plcasaniness)
WEAT 2: Instruments vs Weapons (Peassaineess)
WEAT 6: Male vs Femaal Names (Career vs Family)
WEAT 7: Meth vs Art (Gender Terms)
WEAT 8 Science vs Ar (Gender Terms)
WEAT 9 Mental vs Physical Disease (Duration)
M
clo bm
10
Figure Effect size across languages for Mj (embeddings from the static embedding layer and considering average of subwords) in DistilmBERT. Significant results 95% level of confidence colored and shaded. Negative values of d indicate reversed associations.
WEAT 1 Flowers vs inpects (Pleasantnesss
WEAT 2: Instruments vs Weapons (Pleasantness) WEAT 6: Male vs Female Names (Career vs Family)
pa
bm
Terms)
Terms)
Physical (Duration)
Figure 7: Effect size d across languages for Ms (embeddings of last hidden layer and considering average of subwords) in DistilmBERT. Significant results at 95% level of confidence are colored and shaded. Negative values indicate reversed associations.
Flowers Insects (Pleasantness)
WEAT 2: Instruments Weapans (Pleasantress) WEAT vs (Career vs Family)
WEAT Math
WEAT
Mental Disease (Duration)
2
Figure 8: Effect size across languages for M1 (FastText). Significant results at 95% level of confidence colored and shaded. Negative values of indicate reversed associations.

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B Effect sizes for Human-Centered bias dimensions from DistilmBERT and Fest
sexualit-valeenc
0:33
0.15
10.17
4.700
-1.32
-0.4
-3.10
0.35
-0.85
0.60
sesuallt-perc-eption
0.02
0-48
0.33
0.38
0.39
10.72
0.03
022
2.55
5.12
1.00
0.00
3.36
0.38
0.33
0.70
0.40
0.30
0.54
-1.14
5.60
0.05
0.30
+0.24
D-43
ablelsm enceeee
0.73
0.08
10.73
13.0
0.77
0.34
10.75
2.31
4.34
0.42
+..46
11.50
0..0
1.02
+3.2
+0.20
11.06
0.09
0.33
0.78
00.81
0.14
1.07
-0.51
ablelsm-gee gender
0.89
0.90.0.07
00.00
10.00
0.30
0.30
Immigration
6.92
3.00
0.04
2:37
10.83
0.25
0.97
0.92
education
toxtcity
40.0
0:34
2.48
-221
0.39
0.20
10.43
0.49
-0.82
0:13
.0.1
0.87
0.85
0.03
0.35
.0.9
0..45
1.35
bm
<80
88
2
2
12
2
2
45
15
7
Figure 9: Effect sizes from M2 (embeddings from the static embedding layer and considering average of subwords) for contemporary biases in DistilmEERT; significant biases are indicated in bold, evidencing diverse language- specific trends across all dimensions.
sexualit-vallee ence
4.01
0.35
0.33
10.00
31.43
-3.00
0.18
0.51
0.73
-0.73
resually-perception
1007
0.18
0.35
0.3
0.30
0.42
40.1
022
3.80
5.00
0.87
+0.24
3.39
0.89
0.53
0.20
0.59
0.22
0.39
-0.50
3.40
5.64
0.24
+0.39
0.55
ablelsm valence
8.2
0.70
0.85
0..00
0.31
0.77
0.45
0.71
5.14
0.26
0.20
0.27
1.22
0.05
0.65
0.25
3.25
0.55
0:30
0.87
10.87
0.52
1.25
0.37
gender
0.80
0.95
0.88
5.11
4.34
10.3
-3.37
0.37
0
Immigration
education
toxicity
0.83
10.03
22.5
-.38
0.24
0.22
0.92
4.00
0.42
.084
10.5
0.10
-.40
0.57
4.28
0.13
.0.14
4.33
1.34
88
2
2
8
2
15
Figure 10: Effect sizes from Ms (embeddings of last hidden layer and considering average of subwords) for contemporary biases DistilmBERT; significant biases are indicated bold, evidencing diverse language-specific trends across all dimensions.
sonualty-vallenc b.as
0.33
10.44
0.75
-2.72
0.60
10.30
10.55
0.64
0.44
0.08
-2.20
0.45
0.03
0.82
4..22
0.44
resualty-perceetioon
A0.33
0.26
10.33
0.64
0.27
0.86
0.30
3.36
2.58
5.43
0.27
0.2
1.04
10.05
0.53
-023
+3.43
0.29
0.55
+0.83
-0.12
-0.26
0.90
0.39
+0.03
ablelsm valence
us2
0.50
1.07
2.05
0.11
0.35
0.23
0.44
2.61
2.09
5.00
0.37
0.40
0.59
0.75
0.12
0.38
0.34
1.07
0.43
0.04
0.20
0.30
0.50
0.15
gender
0
Immigration
education
toxicity
4.72
0.002
0:32
-1.39
-..96
5:13
00.76
5.00
40.31
0.775
0.81
4.20
0:73
-031
0.87
0.23
620
88
12
2:
2
22
2
3
Figure 11: Effect sizes for contemporary biases in Mio (FastText); significant biases are indicated in bold, evidencing diverse language-specifie trends across dimensions.

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C Effect sizes for original WEAT categories from XLM-RoBERTa
WEAT 1 Flowers vs ingedss (Plcasaniness)
WEAT 2: Instruments vs Weapons (Peassninesss)
WEAT 6: Male vs Female Names (Career vs Family)
clo
WEAT 7: Meth vs Art (Gender Terms)
WEAT 8 Science vs Ar (Gender Terms)
WEAT Mental vs Physical Disease (Duration)
50
2
Effect size across languages for M, (embeddings from the static embedding layer and considering average of subwords) in XLM-RoBERS Significant results 95% level of confidence colored and shaded. Negative values of d indicate reversed associations.
Figure 12:
(Pleasantneas)
2: Instruments Weapons (Pleasantness)
vs (Career vs Family)
8
55
WEAT Math Ar (Gender Terms)
WEAT 8 Science vs Mt (Gender Terms)
WEAT Mental Physical Disease (Duration)
ah
2=a
bi Del
Figure 13: Effect size d across languages for Ms (average of embeddings from all hidden layers and considering average of subwords) XLM-RoBERII Significant results at 95% level of confidence are colored and shaded. Negative values of d indicate reversed associations.
WEAT 1 Flowers vs Insetts (Plessantness) WEAT 2: Instruments vs Weapons (Plessantness) WEAT 6: Male vs Fermal Names (Career vs Family)
2
=
des
WEAT7 Math (Gender Terms)
WEAT 8 Science vs Art (Gender Terms)
Mental Physical Disease (Duration)
5
888
ZM
B23
5.
Figure 14: Effect size across languages MMs (embeddings last hidden layer considering average subwords) in XLM-RoBERI.. Significant results 95% level of confidence are colored and shaded. Negative values of indicate reversed associations.

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D Effect sizes for Human-Centered bias dimensions from XLM-RoBERTa
sexualit-valeenc
-0.00
0.37
0.00
10.67
5:17
4.300
10.00
-0.30
44.00
+0.10
resually-perceeption
H227
0.02
0.00
10.33
0.22
4.22
10.67
-0.01
2.21
5.37
0.31
+0.43
0.43
0.10
10.47
0.00
0.95
40.20
0.42
-0.23
5.23
+0.05
4.52
0.08
0.70
ablelsm valeenee ence
0.10
0.91
0.11
0.99
-.54
0.54
0.10
10:30
0.77
4.35
0.07
00.83
10.03
0.23
0.01
0.30
0.94
4..0
0.44
-0.12
10.61
4.33
4.25
0.59
1.14
ablelsm-gee gender
0.92
822
22.3
0..08
0.40
0.60
0.89
0.00
0.03
0.30
0.30
Immigration
0.10
5.00
1.00
40.5
13.00
-2.42
education
toxicity
0.72
-2.6
0.47
4037
.012
0.88
4.30
0.03
0.2
-349
4.14
0.25
.0.79
4.30
1.15
<8b
.8
2
2
2
2
15
7
Figure 15: Effect sizes from M1 (embeddings from the static embedding layer and considering average of subwords) for contemporary biases XIMM-RBERRa: significant biases are indicated in bold, evidencing diverse language- specific trends across all dimensions.
sexualit-valeee ence
6.13
-3.48
.0.53
5.28
4.39
+0.15
10.70
0.29
-4.16
resuallt-perceettion
10.2
10.11
20.00
10.02
0.01
0.31
-0.55
0.38
3.69
4..0
44.00
40.33
0.45
0.43
0.79
-3.12
0.92
0.41
0.25
10.00
0.82
-4.10
0.76
0.20
0.81
ablelsm valeene
0.43
0:35
0.20
0.22
201
0.95
0.30
0.15
0.84
0.59
0.80
10.11
3.55
0.43
0.17
0.33
0.35
0.35
0.40
0.92
0.45
0.26
0.00
0.65
055
gender
0.45
10.00
-242
0.33
00.00
0.35
0.60
053
-3.00
0.30
00.7
0.53
0
Immigration
education
toxicity
0.72
0.04
13.07
0.95
-0.98
0.21
.036
10.0
3.45
1001
1..4
0.30
3.47
2.23
4227
0.43
0.20
0.88
0.05
1.27
220
88
2
2
2
2
2
Figure 16: Effect sizes from Ms (average of embeddings from all hidden layers and considering average of subwords) for contemporary biases XI.M-RoBRT significant biases are indicated bold, evidencing diverse language-spent specific trends across all dimensions.
sonualty-vallnne 1152
0.32
2.57
0.23
0.82
5.28
$0.04
00.83
0.53
-2.0
40.77
0.25
0.42
0.02
40,27
resullty-perceptioo
4030
1022
10.55
+0.63
+3.4
4..09
40.2
0.22
0.71
-5.38
0.33
40.38
-0.13
0.27
0.23
-0.33
0.52
0.88
0.34
10-18
-0.00
-0.00
40.37
40.03
D.6E
ablelsm val encee
0.38
0.24
0.5
0.91
0.29
0.72
0.00
0.70
5.07
2.46
0.73
0.30
3.59
0.15
0.90
9.00
0.50
0.15
0.35
0.92
0.09
0.29
0.20
0.82
0.04
gender
0
Immigration
education
toxicity
10.03
22.4%
-0.99
0.30
-0.75
-0.33
5.34
0.43
0.55
2.48
-2.14
0.60
0.07
5.40
0.94
12
2:
2
2
2
15
26
Figure 17: Effect sizes from Ms (embeddings of last hidden layer and considering average of subwords) for contemporary biases in XIMMRRBERTa; significant biases are indicated bold, evidencing diverse language- specific trends across all dimensions.

---

E Comparison of Monolingual and Multilingual models
IndicBERT
distilBERT-muttiinguaa
monolingual BERT
hi
mr
weat6
weat2
weat6
weat2
weat6
weat2
weat7
weatl
weat7
weat1
weat7
weat1
weat8
weat9
weat8
weatg
weat8
weat9
te
weat6
weat2
weat6
weat2
weat6
weat2
weat7
weatl
weat7
22
weatl
weat7
-2
weatl
weat8
weatg
weat8
weatg
weat8
weat9
Figure 18: Monolingual models generally have larger effect sizes across languages and WEAT categories for Ms (average of embeddings from hidden layers and considering average of subwords). Markers show significant results.

---

F
Effect sizes and p-values for discussed results
Method
Effect Size (p-value)
DistilmBERT
1.062 (0.000)
Monolingual BERT
-0.629 (0.996)
Table 5: Effect sizes and p-values for Thai WEAT (Fooverstnsccs.PPsssttllllppeo
Method
Effect Size (p-value)
Method
Effect Size (p-values)
Mi
0.262 (0.299)
M8
-0.240 (0.683)
M2
-0,293 (0.721)
M7
0.838 (0.030)
M3
-0.028 (0.522)
Ms
-0.645 (0.904)
M4
-0.198 (0.651)
My
-0.119 (0.587)
Ms
0.155 (0.378)
M10
0.803 (0.051)
Table 6: Effect sizes and p-values Tagalog WEAT (Math:Art::ale Terms:Female Terms)
Method
WEAT
WEAT 2
DistilmBERT
0.084 (0.380)
-0.164 (0.733)
Monolingual BERT
0.692 (0.003)
0.533 (0.018)
Table 7: Effect sizes and p-values for Turkish WEAT I (PooeessinnetttsPeeestttlllaaaa and WEAT 2
(Instruments: Weattts::aapp::::::aa
Plcaosn:::i Unpllcssat
Method
WEAT 1
WEAT 2
WEAT 6
WEAT 7
WEAT 8
DisTilmBERT
1.352 (0.000)
1.552 (0.000)
0.914 (0.033)
0.959 (0.020)
0.940 (0.023)
Monolingual BERT 1.449 (0.000)
1.511 (0.000)
0.447 (0.194)
1.111 (0.005)
0.976 (0.016)
Table Effect sizes p-values for different WEAT tests for Chinese


Reviews:
[{"review_id": "i7Xo6tzGkb", "reviewer": "Reviewer_L4qs", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for the insightful feedback on our work! We appreciate your acknowledgment of our comprehensive evaluations of WEAT in 24 less-represented languages and our efforts to extend this to address contemporary, human-centered biases. We assure that the main concerns raised are fixable and we will fix them in the final version of the paper. \n\n**Language names not explicitly mentioned:**\n- We apologize for this error and will explicitly mention the language codes in all figures and appendix material in the final version of the paper upon acceptance.\n- The languages (in alphabetical order of language codes) are: Arabic (ar), Bengali (bn), Sorani Kurdish (ckb), Danish (da), German (de), Greek (el), Spanish (es), Persian (fa), French (fr), Hindi (hi), Italian (it), Japanese (ja), Korean (ko), Kurmanji Kurdish (ku), Marathi (mr), Punjabi (pa), Russian (ru), Telugu (te), Thai (th), Tagalog (tl), Turkish (tr), Urdu (ur), Vietnamese (vi), Chinese (zh)\n\n**Anonymous link not working:**\n- We apologize for any inconvenience caused due to the anonymous GitHub link not working, which might be due to outages on the external server it is hosted on.\n- The complete dataset, associated code for the experiments, and the results are provided in a zip file accompanying the original submission (which can be downloaded from the Supplementary Materials section of our submission). We kindly ask you to refer to the provided folder to access examples from our dataset, in case the anonymous link fails to work again. \n\n**Bias sensitivity metric:**\n- We acknowledge the concerns regarding our metric’s description. However, the bias sensitivity metric is not used to measure biased associations in word embeddings. Instead, it is a heuristic for comparing encoding methods. On the other hand, metrics like WEAT and the work by Kurita et al. (LPBS or Log Probability Bias Score) are metrics directly used for measuring biases in word embeddings. Thus, we do not need to and cannot compare them with our bias sensitivity metric.\n- We will clarify these details in the paper by reorganizing section 3.2 and section 3.1 to explain the significance of our metric better.\n\n**Observations in section 4.3:**\n- We regret the ambiguity arising from the presentation of our results. Any observations highlighted in bold in our results are _statistically verified_ outcomes from WEAT, confirmed as significant through $p$-value testing. These results can be cross-referenced in our supplementary code or via the anonymous link. Figures 3, 4 and 5 also refer to these significant biases and their captions explicitly mention this.\n- Following lines 406 and 426, we have provided anecdotal examples. These are intended to highlight how the language-specific biases we statistically identified in language models manifest in both our annotator (a native speaker of the said language) and web searches. While we acknowledge that these examples might be influenced by the individual experiences of the selected annotator, it's crucial to understand that they are presented merely as illustrative supplements to our statistically substantiated conclusions, not as a singular, potentially biased human validation.  We will clarify these points at the beginning of the section, so as to avoid potential confusion.\n\n**Contemporaneous citations suggested:**\n- Thank you for the helpful suggestions. We will include them in the paper to improve our quality of references even more.\n\nWe hope that our explanations above clarify any questions regarding the soundness of our work (as reflected by the low soundness score). If you feel that there are more fundamental issues, please do get back to us with more questions.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "* Extensive evaluation of WEAT to 24 languages, including several languages not commonly studied in the fairness literature. The extension to human-centered contemporary biases is particularly noteworthy. Most of the evaluation is very sounds and backed by a large set of empirical results using the mechanisms and data developed in this work.", "reasons_to_reject": "* The 24 languages mentioned in the paper are not explicitly spelled out anywhere, except for figures in the appendix (and that too using language codes). This is quite a glaring omission.\n* It is difficult to judge the quality of the contribution of the dataset, since the paper does not include a single example, and the anonymous link does not work.\n* The bias sensitivity metric is a little difficult to interpret, since it is not contextualized with respect to previous metrics (e.g. those by Kurita et al.) in the results. While I understand what the metric aims to do, the justification of creating a new metric is unclear. \n* Some observations in Section 4.3 are not backed up very rigorously. For instance, the conclusion in line 406 is driven by a web-search + conversation with a single annotator. Similarly, the conclusion in line 426 is also based on a single annotator. It is unclear to what extent this is influenced by selection bias.", "questions_to_authors": "", "paper_topic_and_main_contributions": "The objective of this paper is to extend WEAT, the classic test for bias in word embeddings, to non-Western/European languages and locales. The paper has several contributions to advance this goal : first, a dataset of target and attribute pairs (like the original WEAT) are created for a subset of previously identified bias dimensions + 5 new dimensions. This dataset is created by semi-automatically translating existing English data to these languages. Second, the WEAT is adapted for multilingual settings by capturing multi-word translations of lexical items in English as well as by identifying a new metric to measure bias. An extensive evaluation of various pre-trained masked language models is carried out on this dataset and several observations about the models and languages studied are presented.", "excitement": 3, "reproducibility": 2, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Missing_References": "* Not penalizing the paper for this, since it is contemporaneous, but [1] and several papers cited in [1] are relevant for future iterations of this work. \n\n[1] - https://arxiv.org/abs/2305.11242"}, "extra_text": {}}, {"review_id": "hwOTYLW0ps", "reviewer": "Reviewer_LAHL", "summary": "", "questions": "a) Why is the BERT's CLS token considered as an encoding for the words? Unless I'm missing something, this token will encode the entire sentence/input rather than only the relevant word.\n\nb) How/Why were the words/terms for the dimensions toxicity, ableism and sexuality chosen?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your valuable feedback on our research! We appreciate your recognition of the impact of our paper and are glad to see that you find the contributions interesting and well-written.\n\n**(a) Additional Target Attribute Pairs for WEATHub:**\n- We apologize for any confusion caused by our presentation of the human-centered contemporary biases. Our methodology for selecting the proposed dimensions, was fourfold: \n\n    **(1)** We start with insights from intersectional biases research (lines 161-163 have relevant citations).\n\n    **(2)** Then we engage in discussions with native speakers of the 24 languages establishing a foundational consensus on bias dimensions across linguistic communities.\n\n    **(3)** Subsequently, we formulated the corresponding target/attribute pairs after weighing different options for naming them.\n\n    **(4)** Finally, we opted for a naming convention that we felt best depicted these biases.\n\n- Representing biases across cultures under a single umbrella is a nuanced task. Our work represents a first step in this direction, and we will explore other combinations of target/attribute pairs.\n- Your understanding of the toxicity dimension is exactly as we intended. For sexuality bias, our objective was to highlight biases against LGBTQ+ groups and for ableism bias we designed it to spotlight distinct challenges faced by women with disabilities.\n- We understand that these framings need clearer elaboration, so we’ll incorporate clarifications and new experiments on valence in the final version of our paper on acceptance. With word lists for valence already available in our data for all the 24 languages, these experiments would enhance our paper and align it with recent research on valence like https://arxiv.org/pdf/2307.03360.pdf.\n- We already have the results for the main paper experiment setting for ableism and sexuality corresponding to Figure 5 as below. The appendix results for the same would be updated in the final version of the paper.\n\n**(1)** Ableism valence (Insult:Disability :: Unpleasant:Pleasant) results (effect sizes) for Method M5 (avg of all hidden layers of DistilmBERT and subword avg) bolded if significant at 95% level of confidence in alphabetical order of language codes similar to Figure 4 for all the 25 languages (including English) :\n\n| ar  | bn   | ckb  | da   | de   | el   | en   | es   | fa   | fr   | hi   | it   | ja    | ko   | ku   | mr   | pa   | ru   | te   | th   | tl   | tr   | ur   | vi    | zh   |\n|-----|------|------|------|------|------|------|------|------|------|------|------|-------|------|------|------|------|------|------|------|------|------|------|-------|------|\n| -0.68 | **0.78** | **0.86** | -0.74 | -0.65 | **0.79** | 0.48 | 0.65 | 0.28 | -0.05 | 0.10 | -0.51 | **-1.36** | -0.25 | **0.79** | 0.25 | -0.35 | **-0.94** | -0.04 | 0.92 | 0.25 | **-0.73** | 0.54 | **1.23** | -0.51 |\n\n\n   **(2)** Sexuality valence (LGBTQ+:Straight :: Unpleasant:Pleasant) results (effect sizes) for Method M5 (avg of all hidden layers of DistilmBERT and subword avg) bolded if significant at 95% level of confidence in alphabetical order of language codes similar to Figure 4 for all the 25 languages (including English):\n\n| ar   | bn   | ckb  | da   | de   | el   | en   | es   | fa   | fr   | hi   | it   | ja    | ko   | ku   | mr   | pa   | ru   | te   | th   | tl   | tr   | ur   | vi   | zh   |\n|------|------|------|------|------|------|------|------|------|------|------|------|-------|------|------|------|------|------|------|------|------|------|------|------|------|\n| -0.12 | -0.01 | -0.29 | 0.20 | 0.15 | -0.31 | -0.02 | 0.12 | -0.55 | 0.16 | **-0.79** | 0.03 | **-1.42** | -0.63 | -0.38 | 0.40 | -0.40 | 0.15 | -0.34 | -0.02 | 0.34 | **0.75** | -0.45 | **-0.76** | 0.67 |\n\n**(b) Validation of results:**\n- We regret the ambiguity arising from the presentation of our results. Any observations highlighted in bold in our results are _statistically verified_ outcomes from WEAT, confirmed as significant through $p$-value testing. These results can be cross-referenced in our supplementary code or via the anonymous link. Figures 3, 4 and 5 also refer to these significant biases and their captions explicitly mention this.\n- The anecdotal examples (lines 410-424 and 442-446) are intended to highlight how the language-specific biases we statistically identified in language models manifest in both our annotator (a native speaker of the said language) and web searches. While we acknowledge that these examples might be influenced by the individual experiences of the selected annotator, it's crucial to understand that they are presented merely as illustrative supplements to our statistically substantiated conclusions, not as a singular, potentially biased human validation.  \n- We will clarify these points in our paper in section 4, so as to avoid potential confusion.\n\n**Responses to your Questions:**\n\n**(a) BERT CLS:**\n- As you rightly point out, it is common to use “contextualized embedding” to refer to embeddings from BERT in a sentential context. Instead, we are directly providing the word (or phrase in some cases) as input for the embedding method.\n- How does this make the resulting embedding different from the 7 other BERT hidden layer-based approaches we have compared in our work? In all the other methods, we choose either the first subword or the average of the subwords. If we encounter phrases in the input, we average the embeddings across the different tokens in the phrase after choosing a corresponding subword strategy. The CLS-based approach is the only method where we consider the context of all the subwords and all the tokens in a phrase to get a single embedding from a contextual model like BERT without any averaging or selections involved. \n- We will clarify this detail in the paper in section 3.2 to avoid any potential confusion.\n\n**(b) Words for Toxicity, Ableism, Sexuality:**\n- Lines 189-198 provide a brief overview of our approach to creating the list of words for each of the new bias dimensions. \n- To expand on that, as discussed in our response (about additional Target/Attribute Pairs in WEATHub) above, Firstly, we crafted potential names for the dimensions and the corresponding target/attribute class names. Subsequently, we devised an initial set of English terms for each category by amalgamating information from online platforms such as Wikipedia and the studies referenced in lines 161-163. This preliminary set was refined based on discussions with our annotators and by querying various open-source and closed-source large language models. The ultimate selection of words signifies a shared understanding and agreement between our team and annotators.\n- As discussed before, we will include experiments in valence measurements for these dimensions to keep our work on par with contemporary research and make it more intuitive.\n\nWe hope that our explanations above clarify any questions regarding the soundness of our work (as reflected by the low soundness score). If you feel that there are more fundamental issues, please do get back to us with more questions!", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "a) Multiple contributions and extensive experiments\n\nb) Interesting, relevant results\n\nc) Mostly well-written paper", "reasons_to_reject": "a) Additional Attribute-Target pairs for WEATHub seem partially unintuitive:\nThe bias dimensions toxicity and ableism are characterised by the association between female/male terms and offensive/respectful or insult/disability words, respectively. While I can somehow understand this in the case of toxicity (females may more often be the target of toxicity, even thoough I'm not entirely conviced this is what's supposed to be shown here), I don't really understand it for the ableism dimension. Similarly, the sexuality dimension tests the association between LGBTQ+/straight words and prejudice/pride, which doesn't seem intuitive to me.\n\nb) Validation of results:\nThe results of the bias detection are not sufficiently validated. While the findings are interesting, I'm not convinced that the biases discovered by the method are actually correct. In some cases, they are validated anecdotally by asking one of the native speakers serving as annotators, but this is not convincing enough to me - I would not be comfortable deciding whether there is generally a bias with regards to a group of people, objects, etc. in my native language.\n\nOverall, the paper makes interesting contributions and will be impactful due to its resources. However, I think that the issues raised above should be fixed before acceptance.", "questions_to_authors": "a) Why is the BERT's CLS token considered as an encoding for the words? Unless I'm missing something, this token will encode the entire sentence/input rather than only the relevant word.\n\nb) How/Why were the words/terms for the dimensions toxicity, ableism and sexuality chosen?", "paper_topic_and_main_contributions": "The paper makes multiple contributions: extending the Word Embedding Association Test WEAT's word lists to multiple additional languages and dimensions, modifying its formulas to accomodate these additions and employing this new resource to perform bias analysis in several settings.", "excitement": 4, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "xTcffbRxUS", "reviewer": "Reviewer_LVwA", "summary": "", "questions": "1. ~It seems to me like the Table 2 definition of Ableism is actually more a question of positive vs negative instead of gender? They use \n\"insult:disability term::male:female\" whereas I'd suggest something like \"insult:disability term::unpleasant:neutral/pleasant\" as a way to\ntest for implict ableism in a model. Can you defend this choice or is this a typo?~ **Authors have added valence correlation in addition to gender correlation for ableism results. I hope that they will integrate this into the core findings and discussion, and provide their defense for the disability-gender correlation in the camera ready as they provided in their response.**\n2. Are the sustained language-level positive/negative biases found in the contextualized embeddings (366-377) expressing language-level biases?\nOr do you think they are genuinely reflective of differences in valence expressed in those languages in the training corpora directly (which I suppose is still a bias but would be addressed differently)?\n3. Please consider reorganizing your presentation of methods and results, give the reader a clear understanding of what tangible things you will test (languages, models, etc) **on page 1** and then **make it clear which results are a part of that main experiment, and which are auxilliary findings**. See my \"Confusing mixture\" point in Reasons to Reject\n4. Why not consider the India-specific biases in English?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank you for taking the time to write a detailed review of our work! We will address the presentation issues that you have mentioned and are very happy to hear that you think this work should be published.\n\n**1. Reorganize introduction and method to introduce motive better:**\n- The first paragraph of the introduction mentions large language models to try to portray the problems of biases in these models as found by other studies. However, since our work does not focus on LLMs or generative outputs, we will modify this to make it more suited to our use case. The latter part of our intro focuses on explaining our contributions. Still, we understand how it might feel disconnected from the beginning discussion, and we will work to make it connect together better from a reader’s perspective.\n\n- Section 3.1  (Methods) briefly introduces the WEAT metric for readers unfamiliar with Ethics/Biases research. We included the discussion of $p$-values initially considering the same reader group. We like your suggestion that it will be helpful to have details of all the languages and the models in this space instead, along with an intuition for the metric. We will reorganize section 3.1 to accommodate this change. This will also solve the problem of having to start off the results section with a discussion of “Here are the models we tested,” as you mentioned.\n\n**2. Better flow in the results section:**\n- We appreciate your comment about how the results might appear to be mixed in randomly. Per your suggestions, we will reorganize section 4 and its subsections to have a clearer structure for the main results. The extra page for the camera-ready should help in managing that.\n\n**3. Figures are labeled with WEAT classes:**\n- We appreciate your feedback in helping us make our figures better. We will replace the usage of acronyms with the corresponding names wherever possible and update figure captions otherwise to make the figures more readable so that the dependencies on Tables 1,2 and 3 can be reduced.\n\n**4. Framing of ableism w.r.t. gender:**\n- After consulting with our annotators across all languages, we established our framing dimension. The consensus was that when discussing disabilities, terms such as \"blind\" or \"visually impaired\" tend to be used differently across genders in their respective languages.\n- Framing ableism in terms of valence might offer a perspective that's more intuitively graspable. We intend to undertake these experiments and incorporate the findings into the finalized version of our paper, in addition to the current framing.\n\n**Responses to your Questions:**\n\n**(1)** As discussed in point 4 of our reply above, we framed our dimensions based on the research we came across and the general opinion from conversations with native speakers in each of the languages. The resulting angle of comparing ableism w.r.t. gender is agreeably less intuitive than comparing ableism w.r.t. valence (pleasant: unpleasant). Running these valence experiments for the ableism dimension is straightforward because we already have the data for all languages. The main results of these experiments are given below. We will update the results in the appendix with the corresponding results in the final version of the paper.\n\nAbleism valence (Insult:Disability :: Unpleasant:Pleasant) results (effect sizes) for Method M5 (avg of all hidden layers of DistilmBERT and subword avg) bolded if significant at 95% level of confidence in alphabetical order of language codes similar to Figure 5 for all the 25 languages (including English) :\n\n| ar  | bn   | ckb  | da   | de   | el   | en   | es   | fa   | fr   | hi   | it   | ja    | ko   | ku   | mr   | pa   | ru   | te   | th   | tl   | tr   | ur   | vi    | zh   |\n|-----|------|------|------|------|------|------|------|------|------|------|------|-------|------|------|------|------|------|------|------|------|------|------|-------|------|\n| -0.68 | **0.78** | **0.86** | -0.74 | -0.65 | **0.79** | 0.48 | 0.65 | 0.28 | -0.05 | 0.10 | -0.51 | **-1.36** | -0.25 | **0.79** | 0.25 | -0.35 | **-0.94** | -0.04 | 0.92 | 0.25 | **-0.73** | 0.54 | **1.23** | -0.51 |\n\n**(2)** Lines (366-377) speak about the biases we find in languages by querying language models. We believe this might be due to the biases or the lack of them expressed in those languages in the training corpora. Still, the training corpora for a given language could have originated from human-written text at some point, implying that the biases in the training corpora might be directly reflective of the language level (or community-level) biases. We will update the wording of this paragraph to make this more obvious.\n\n**(3)** We will restructure the introduction, methods, and results to address the presentation issues that you have mentioned and make the paper more accessible to readers.\n\n**(4)** This is an interesting question because we could potentially consider local (country or culture) specific biases in the local language or any language that is not as commonly spoken within that locale. For example, we might choose to evaluate India-specific biases not just in English but also in the other languages in our dataset. Our work represents a first step towards comprehensive bias evaluation across languages, and the evaluation of country-specific biases in other languages is something that we are working on actively.  We will add the results for India-specific biases in English to the final version of our paper to make it more in line with all the other experiments.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "Novel human-centered bias analysis. Great job widening the applicability of these bias classes by looking in the multilingual setting and including the bit focusing on India-specific biases.\n\nWell-presented bolded findings in results section (apart from my gripes). They propose some hypotheses for why different observations are made between languages, although some discussion of them is thin.\n\nInteresting results, novel generalization of WEAT to multilingual setting, novel bias classes.\n\nUseful resource in WEAThub", "reasons_to_reject": "**Post-rebuttal comments in bold: I believe the authors have satisfactorily responded to my reasons to reject and have updated my score.**\n\n**the authors have responded to my complaints regarding organization and plan to reorder things in line with my and other reviewers suggestions. I think this will improve the quality of the paper considerably.** ~Intro, method sections are so vague in parts as to be confusing. From reading the intro alone I expected this would be a study of generative LM outputs and the differential expression of biases under translation into the languages, but in fact it's just a generalization of the WEAT test into 24 languages with culturally specific knowledge and a few tweaks to handle contextualized embeddings like BERT. That's fine, but the paper would be more readable if the abstract/intro were more upfront about this. In the methods section a lot of ink is spilled explaining p tests which I feel like is redundant for people who have taken stats 101 and useless for those who haven't. This space would be better spent doing things like:~\n\n- ~listing all the languages and models they tested in the intro (see later point about \"Confusing mixture\")~\n- ~giving background intuition on how WEAT works/is intended to work (eg. \"WEAT purports to identify biases based on the relative proximity of word embeddings in latent space between words of negative valence and classes against which social bias is expressed in the real world\" or something)~\n\n**Authors plan to reorder results and introduce core elements earlier (eg in section 1 or 2) to make the results section less cluttered. This will improve readability and quality considerably.** ~Confusing mixture of discussion of the core result (bias in XLMBERT, which layers, which language etc) and auxilliary discussion points/findings along the way to the main result (need for human translation, some expressions are untranslatable, etc). I *love* that these extra points get discussed, it's just a frustrating read to have them mixed in seemingly at random (this is worst in secion 4, where they go from \"Here are the HuggingFace models we tested\" to \"our study compares human translation and Google translate\" to \"these are the patterns we find\". Please reorganize).~\n\n**Authors have stated they will redo figures to be readable at a glance with descriptive labels rather than class numbers.** ~Figures are labeled with just WEAT classes, which leads to a lot of uninterpretable acronyms. (\"weat 9\" instead of \"mental/physical disease bias\")~\n\n**Authors have addressed this (see questions for authors 1)** ~Framing of ableism as associating terms of disability with female is weird. See my first question for the authors.~", "questions_to_authors": "1. ~It seems to me like the Table 2 definition of Ableism is actually more a question of positive vs negative instead of gender? They use \n\"insult:disability term::male:female\" whereas I'd suggest something like \"insult:disability term::unpleasant:neutral/pleasant\" as a way to\ntest for implict ableism in a model. Can you defend this choice or is this a typo?~ **Authors have added valence correlation in addition to gender correlation for ableism results. I hope that they will integrate this into the core findings and discussion, and provide their defense for the disability-gender correlation in the camera ready as they provided in their response.**\n2. Are the sustained language-level positive/negative biases found in the contextualized embeddings (366-377) expressing language-level biases?\nOr do you think they are genuinely reflective of differences in valence expressed in those languages in the training corpora directly (which I suppose is still a bias but would be addressed differently)?\n3. Please consider reorganizing your presentation of methods and results, give the reader a clear understanding of what tangible things you will test (languages, models, etc) **on page 1** and then **make it clear which results are a part of that main experiment, and which are auxilliary findings**. See my \"Confusing mixture\" point in Reasons to Reject\n4. Why not consider the India-specific biases in English?", "paper_topic_and_main_contributions": "The authors expand the word embedding association test (WEAT) to 24 global south languages, with a particular focus on Indian languages,\nand move from WEAT's focus on static embeddings to the contextualized represenations contained in language models through a behavioral\nanalysis of translation between the languages studied. Additionally, they compare machine translation into those languages against human translation.\nFinally, they analyze culturally-specific embedded information in the model.\n\nThey use human annotators to correct machine translations of WEAT words into 24 languages, testing classes against attributes by comparison,\neg \"flowers:insects::pleasant:unpleasant\" or \"math:art::male:female,\" identifying both innocuous and biased types of implicit word association.\nAdditionally, they test for ableism, toxicity, and sexuality, education, and immigration status biases.\n\nThey additionally test specifically for India-specific biases such as casteism and surname stereotypes that are unlikely to occur in non-Indian \nlanguages, and compare their expression in Hindi (India's dominant local language) against other Indian languages such as Marathi and Telugu.\n\nThey introduce a bias sensitivity metric, the mean of the variances for the cosine similarities of embeddings between each set of classes (?) My \nunderstanding of this was muddled until I checked back to the original WEAT paper, I would suggest the authors explain how the WEAT test works a \nbit more in the intro to make the paper accessible to those outside the Ethical NLP space.\n\nThey find a few main results as well as many specific ones:\n\n- contextualized word embeddings (eg higher layers in bert) do indeed express bias differently from static embeddings\n- high-resourcedness makes representation biases more consistent in multilingual vs monolingual models as opposed to low resourced languages\n- monolingual models tend to express more bias than multilingual ones (or would a better framing be \"multilingual training mitigates some biases\"?)\n- Differential representation of gender, human-centric biases across Indian languages.\n\n**I would really like to see the presentation issues I mentioned here addressed, but overall I think this paper should be published. By addressing the issues in reasons to reject, I think the authors can make their paper much more well-recieved and impactful.**", "excitement": 4, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Missing_References": "N/a"}, "extra_text": {"Typos_Grammar_Style_and_Presentation_Improvements": "~Please provide information in the figure captions (or preferably, directly in the charts) about tests instead of just saying things like \"M2\" or \"weat5\". Figure 3 would be SO much better if instead of weat9 the caption said \"Physical disease bias.\"~\n**Authors have stated they will fix this in the camera-ready.**"}}]

Author rebuttal:

Meta-review: This paper expands Word Embbedding Association Test (WEAT) by translating from English to 24 languages. Overall, this paper makes good contributions by 1) proposing a new resource for bias and fairness evaluation through multiple dimensions, 2) empirical results on pretrained masked language models on wide range of languages. During the rebuttal, multiple concerns were raised about presentation clarity (e.g., flows of the results section), but we believe this is an easy fix to make the paper stronger. In summary, the AC is looking forward to the proposed datasets to be contributed to the community.
Decision: Accept-Main