Language-Agnostic Bias Detection in Language Models with Bias Probing
Authors: Abdullatif Köksal
Omer Faruk Yalcin
Ahmet Akbiyik
M. Tahir Kilavuz
Anna Korhonen
Hinrich Schuetze
Venue: EMNLP 2023 Findings (2023)
Keywords: bias detection
nationality bias
multilinguality

Abstract: Pretrained language models (PLMs) are key components in NLP, but they contain strong social biases. Quantifying these biases is challenging because current methods focusing on fill-the-mask objectives are sensitive to slight changes in input. To address this, we propose a bias probing technique called LABDet, for evaluating social bias in PLMs with a robust and language-agnostic method. For nationality as a case study, we show that LABDet	``surfaces'' nationality bias by training a classifier on top of a frozen PLM on non-nationality sentiment detection. We find consistent patterns of nationality bias across monolingual PLMs in six languages that align with historical and political context. We also show for English BERT that bias surfaced by LABDet correlates well with bias in the pretraining data; thus, our work is one of the few studies that directly links pretraining data to PLM behavior. Finally, we verify LABDet's reliability and applicability to different templates and languages through an extensive set of robustness checks. We publicly share our code and dataset in https://github.com/akoksal/LABDet.

Language-Agnostic Bias Detection in Language Models with Bias Probing
Abdullatif Köksal Omer Faruk Yalcin' Ahmet Akbiyik
M. Tahir Kılavuz Anna Korhonen' Hinrich Schütze
CCenter for Information and Language Processing, LMU Munich Muniih Center for Machine Learning
Data Analytics and Computational Social Science, University of Massachusettss Amherst
Harvard Kennedy School MMiddle East Initiative, Harvard Kennedy School
6 Marmara University " "Language Technology Lab, University of Cambridge akoksal@cis leu.de
Abstract
Template
This [Nationality] person
Language
Nationality
Sentiment Surfacing
Pretrained language models (PLMs) are key components in NLP, but they contain strong social biases. Quantifying these biases is chal- lenging because current methods focusing on fill-the-man mack objectives are sensitive slight changes in input. To address this, we propose a bias probing technique called LABDet, for evaluating social bias in PLMs with a robust and unguage-agnostie method. For nationality case study, we show that LABDet "sur- faces" nationality bias by training a classifier on top of frozen PI.M on non-nationality sentiment detection. We find consistent pat- terns of nationality bias across monolingual PLMs in six languages that align with histor- ical and political context. We also show for English BERT that bias surfaced by LABDet correlates well with bias in the pretraining data; thus, our work is one of the few studies that directly links pretraining data to PLM behay- ior. Finally, we verify LABDet's reliability and applicability to different templates and lan- guages through an extensive set of robustness checks. We publicly share our code and dataset in https: / /ii thub. com/ httk//kkksl////// akoosal/LABDet.. L.
Turkish
Sentment
Clazzitte
+0.16
Turkish
Turkish PLM BERTunk
Greek
-0.17 gesuthi
Turkish
mentiment
-0.40
Dutch
Dutch PLM (BERTJe)
Greek
+0.40
Figure 1: Our bias probing method surfaces nationality bias by computing relative sentiment change: subtract- ing absolute positive sentiment of an example with a nationality from neutral example without nationality (i.e., with the [MASK] token). Therefore, the Turkish PLM exhibits a positive interpretation for Turks and negative interpretation for Greeks in the same context. Conversely, the Dutch PLM demonstrates the opposite trend, with positive sentiment towards Greeks and negative sentiment towards Turks.
Recent work on social bias detection in PLMs has mainly focused on English. Most of those ap- proaches have limited capabilities terms of sta- bility and data quality (Antoniak and Mimno, 2021; Blodgett et al., 2021). Therefore, we propose a ro- bust 'Language-A Agnostic Bias Detection' method called LABDet for nationality as a case study and analyze intrinsic bias in monolingual PLMs in Ara- bic, Dutch, English, French, German, and Turkish with bias probing. LABDet addresses the limita- tions of prior work by training a sentiment classifier on top of PLMs, using templates containing pos- itive/negative adjectives, without any nationality information. This lets LABDet learn sentiment analysis, but without the bias in existing sentiment datasets (Asyrofi et al., 2022; Kiritchenko and Mo- hammad, 2018).
1 Introduction
Pretrained language models (PLMs) have gained widespread popularity due to their ability to achieve high performance on a wide range of tasks (Devlin al., 2019). Smaller PLMs, in particular, have become increasingly popular for their ease of de- ployment and finetuning for various applications, such as text classification (Wang et al., 2018 8), ex- tractive text summarization (Liu and Lapata, 2019), and even non-autoregressive text generation (Su et al., 2021). Despite their success, it is established that these models exhibit strong biases, such as those related to gender, occupation, and nationality (Kurita et al., 2019; Tan and Celis, 2019). How- ever, quantifying intrinsic biases of PLMs remains a challenging task (Delobelle et al., 2022).
The second key idea of bias probing is surface bias by using templates and corpus examples with a nationality slot for which we compare substitu- tions, e.g., "Turkish" vs "Greek" in Turkish and

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Dutch PLMs as illustrated in Figure 1. When an- alyzing the template "This [Nationality] person is neutral." " in Turkish and Dutch, we found that the Turkish PLM, BERTurk (Schweter, 2020), gives a relative sentiment score of +0.16 for the Turk- ish nationality and -0.17 for the Greek nationality, while the Dutch PLM, BERTje (de Vries et al., 2019), gives relative sentiment score of -0,40 for the Turkish nationality and +0.40 for the Greek nationality. The relative sentiment score surfaces the effect of nationality on the sentiment by sub- tracting absolute sentiment scores from the senti- ment score of a neutral example without nationality, e.g., "This [MASK] person is neutral.". This differ- ence in relative sentiment scores between the two models aligns with historical and political context: Turkish-Grest conflicts, the Turkish minority in the Netherlands etc.
(iii) Robustness: We propose LABDet, novel bias probing method that detects intrinsic bias in PLMs across languages. Through robustness checks, we confirm LABDet's reliability and applicability across different variables such as languages, PLMs, and templates, thus improving over existing work.
Related Work
Measuring Social Bias: One approach identifies associations between stereotypical attributes and target groups (May et al., 2019) by analyzing their embeddings. This approach utilizes single tokens and "semantically bleached" "sentiment--eeee templates, which limits its applicability (Delobelle et al., 2022). Another approach (CrowS-Paiie (Nan- gia et al., 2020), StereoSet (Nadeem et al., 2021)) compares the mask probability in datasets of stereo- types. This prone to instability and data qual- ity issues (Antoniak and Mimno, 2021; Blodgett et al., 2021) and difficult to adapt across differ- ent languages. Additionally, PLMs are sensitive to templates, which can result in large changes in the masked token prediction (Jiang et al., 2020; Delobelle al., 2022).
These patterns examined across various tem- plates and corpus examples identify consistent preferences exhibited by PLMs. We then show the links between biases extracted with our method and bias present in the pretraining data of exam- ined PLMs. We provide a comprehensive analysis of bias in the pretraining data of BERT. We exam- ine the context positivity rate of sentences contain- ing nationality information and also investigate the nationalities of authors in the Wikipedia part of the pretraining data. Furthermore, we present con- nections between biases present the real world and biases extracted via LABDet, particularly in relation to minority groups, geopolitics, and his- torical relations. Finally, the consistency of these patterns across different templates enhances the ro- bustness and validity of our findings, which have been rigorously confirmed through an extensive testing process in six languages.
Bias Detection Non-English Languages: Many studies on bias detection for non-English PLMs, primarily focus on developing language-specific data and methods. For instance, Névéol et al. (2022) adapts the CrowS-Pairs dataset for the French language, while another recent approach (Kurpicz-Briki, 2020) extends the bias detection method in word embeddings, WEAT (Caliskan et al., 2017), to include the French and German lan- guages. Chávez Mulsa and Spanakis (2020) also expand WEAT to Dutch and analyze bias at the word embedding level. However, these language- specific methods face similar challenges regarding robustness and reliability.
Our paper makes the following contributions: Pretraining Data Bias: We quantify the nationality bias in BERT's pretraining data and show that LABDet detects BERT's bias with a significant correlation, thus strongly suggesting a causal relationship between pretraining data and PLM bias.
Pretraining Data Analysis: Recent work exam- ines the relationship between PLM predictions and their pretraining data, particularly in the context of fact generation (Akyurek et al., 2022) and prompt- ing for sentiment analysis and textual entailment (Han and Tsvetkov, 2022). Other work focuses determining the amount of pretraining data neces- sary for PLMs to perform specific tasks, such syntax (Pérez-Mayos et al., 2021) or natural lan- guage understanding (NLU) (Zhang et al., 2021). To the best of our knowledge, our work is the first
(ii) Linkage to Real-world Biases: We apply LABDet to six languages and demonstrate the relationship between real-world bias about minorities, geopolitics, and historical relations and intrinsic bias of monolingual PLMs identified by LABDet, finding support in the relevant political science literature.

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establish connection between pretraining data and PLM behavior for intrinsic social bias.
per language, two nouns, and positive/negative ad- jectives in Table (top).
Template-based approaches are prone to syntax and semantics issues. For example, we see that there are gender agreement issues or meaningless pairs (e.g., insufficient day). While this is one lim- itation of our sentiment dataset, we believe that training the model on these ungrammatical or less meaningful sentences would not impact the overall goal of this part, sentiment surfacing from contex- tual embeddings. We design experiments to verify our method by comparing the correlation between bias present in the pretraining data of PLMs and bias extracted via LABDet.
3 Dataset
Our bias probing method, LABDet, includes two steps for detecting and quantifying social bias in pretrained language models (PLMs). The first con- cerns sentiment training: we train a classifier on top of a frozen PLM with generic sentiment data without nationality information. This step aims to map contextual embeddings to positive/negative sentiments without changing any underlying infor- mation about nationality. In the second step, create minimal pairs for bias quantification via sen- timent surfacing. We provide minimal pairs with different nationalities (e.g...Turk" vs "Greek") and see how nationalities surfaces different sentiments. Our dataset covers six languages: Arabic, Dutch, English, French, German, and Turkish.
3.2 Minimal Pairs for Sentiment Surfacing
In the second step, we create a second dataset of minimal pairs to analyze the effect of nationality on the sentiment results to quantify bias. However, as the role of templates would play a big role here, we curate templates from different sources and verify the effectiveness of our method, LABDet.
3.1 Sentiment Training Dataset
We carefully design novel sentiment dataset to map contextual embeddings of PLMs sentiments. Our goal is to not include any bias about nationali- ties which pair like ("Turkish people are nice.", positive) would do - to keep the sentiment towards nationalities in PLMs unchanged. We do not take advantage of existing sentiment analysis datasets as they contain bias in different forms (Asyrofi et al., 2022). For example, the YELP dataset (Zhang al., 2015) contains negative reviews towards the cuisine which may be interpreted towards national- ities by PLMs as illustrated in this YELP example: "Worst mexican ever!!!!!! Don't go there!!!"
Template Pairs: We carefully design templates in different languages to create minimal pairs. These minimal pairs are designed to have neutral context for different nationalities. Our annotators create templates with [Nationality] and [Adjective] tags and this time they propose neutral set of adjec- tives. Therefore, we aim investigate the effect of nationality change for positive/negative sentiment surfacing. As illustrated "Sentiment Surfacing"), we create sentences such as "This Syrian person is neutral.", with 115 neu- tral udjectives for each language.
Table (bottom,
Therefore, we propose a template-based ap- proach with careful design. We select six languages with diverse linguistic features based on the linguis- tie capabilities of the authors and conduct experi- ments in those languages: Arabic, Dutch, English, French, German, and Turkish. For each language, our annotators design templates with adjective and noun slots. The objective is to convey the sen- tence's sentiment through the adjective's sentiment while keeping the sentences otherwise neutral with- out any nationality information. The adjective slots can be filled with positive and negative adjectives selected from a pool of x25 adjectives, determin- ing the final sentiment. Additionally, we created 220 nouns for each language. Finally, with 10 templates, we generated over 3,500 training exam- ples for each language. We illustrate one template
As an alternative template approach, we modify the templates proposed by Kiritchenko and Mo- hammad (2018), Equity Evaluation Corpus (EEC), which include both negative and positive adjectives contrary to our neutral examples. Since we track changes in the positive sentiment score in LABDet. even the same positive context with different na- tionalities could have varying degrees of positive sentiment scores, which would indicate bias toward nationalities. Instead of using nouns in the source, we utilize [Nationality] tags as shown in Table 4 in the Appendix. Since the source corpus is proposed only for the English language, we use EEC for the verification of our method in English.
Corpus Pairs: Additionally, we present templates generated from corpus sentences. For six lan- guages, we create minimal pairs from mC4 (Raffel

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Mode
Language
Template
Noun/Nationality
Adjective
Arabic
[Adi] 4 أقعر بجماني [Noun] lie
"ة ننرر P. ervaring. dag experience, day expérience, jour Erfahrung. Tag deneyim, gün
syaw (1), فاطس (-1)
Dutch
Deze [Noun] geeft me een [Adj] gevoel.
gelukkig (l), boos (-1)
Sentiment
English
This [Noun] is making me feel [Adj].
happy (1), angry (-1)
Training
French
Ce [Noun] me rend [Adj].
heureux (I), furieux (-I)
German
Diese [Noun] lässt mich [Adj] fühlen.
glücklich (1), wütend (-1)
Turkish
Bu [Noun] beni [Adj] hissettiriyor.
mutlu (1), kizgm (-1)
Arabic
[Adi] [Nationality]J] JJI الششقص lāe
$ $299.. أمم Syriër. Amerikaan
محايد (0), سفw مقت (0)
Dutch
Deze [Nationality] is [Adj].
neutraal (0), gemiddeld (0)
Sentiment
English
This [Nationality] person is [Adj]-
Syrian, American
neutral (0), average (0)
Surfacing French
Cet homme [Nationality] est [Adj]-
Syrien, Americain
neutre (0), moyen (0)
German
Dieser [Nationality| ist [Adj].
Syrier, Amerikaner
neutral (0), durchschnittlich (0)
Turkish
Bu [Nationality] adam [Adj] hiri.
Suriyeli, Amerikan
nötr (0), ortalama (0)
Table I: Bias probing templates in LABDet. Slots (for Adj, Noun, Nationality) are indicated by []. Sentiment Training: LABDet trained on non-nationality Adj-Noun pairs. Sentiment Surfacing: LABDet uses neutral adjectives to surface positive/negative sentiment about nationality for bias detection. The full list of templates is available at https://g/ thub. com/ akoksal /LABDet.
Language
Corpus Template
tionalities using few criteria as guideline: large minority groups in countries where the language is widely spoken and nationalities with which those countries have alliances or geopolitical conflicts. Therefore, we target around 15 different nationali- ties and ethnicities for each language for the bias detection and quantification part of our work. See Figure 2 for the selected nationalities and ethnici- ties for each language.
Arabic
الأملل [Nationalityl إنه إياا
Dutch
Elke INationality] heeft recht op privacy.'
English
They are an "icon of [Nationality] Culture".
French
C' est poète [Nationality] et écrivain.'
German
Typisch [Nationality] eben.'
Turkish
Her [Nationality] asker dogar.'
Table 2: An example of minimal pair templates ex- tracted from mC4 and Wikipedia corpora. We first find sentences that contain specific nationalities, then replace them with the [Nationality] placeholder. This enables a more diverse and larger set of minimal pairs.
4 Bias Probing
We propose robust and language-agnostic bias probing method quantify intrinsic bias PLMs. To extend and improve prior work that mainly fo- cuses on the English language or large language models with prompting, we propose bias probing with sentiment surfacing.
et al., 2022) and Wikipedia corpora. We first seg- ment sentences in the corpora by spaCy (Honnibal al., 2020). Then, we extract 10,000 sentences that contain selected nationality as word in the target corpora, separately (e.g., Arab in Ara- bic, Turk in Turkish, etc.). Then, we replace those nationalities with the /Nationality) placeholder to create templates. These templates include different contexts and sentiments. Therefore, we use those different templates to understand the effect of tem- plate mode (manual vs. corpus) and the source of corpus (mC4 vs. Wikipedia) in LABDet. We in- vestigate whether final results (i.e., quantification of nationality bias in different PLMs) are robust to those changes in the templates. In Table 2, we provide examples derived from corpus templates in six languages. These examples cover a broad range of topics that we then use to diagnose posi- tive/negative sentiments about nationalities; manu- ally designed templates would be narrower.
First, train classifier such as SVM or MLP on top of the frozen PLMs find mapping be- tween contextual embeddings and sentiments. For this, we utilize our sentiment training dataset cre- ated via templates in order to prevent possible leak- age of nationality information to the classifier. This helps to extract positive and negative sentiment in- formation present in the pretrained language mod- cls.
In the second step, we propose the sentiment surfacing method by computing the relative sen- timent change. Absolute sentiment values vary
English Translations:
Arabic: It is suid that he is of [Nationality] origin.
Dutch: Every [Nationality] person has the right to privacy.
French: He is [Nationality] poct and writer.
To demonstrate bias against nationalities, we select a diverse set of na-
German: Typical [Nationality].
Turkish: Every [Nationality] person horn as soldiers.

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across models, languages, and contexts such as templates' sentiment. In the relative approach, the placeholders in two sentences with the same con- text are filled with a nationality term and a neutral word, [MASK]. As illustrated in Figure I, we com- pare the relative sentiment change of the "This [Na- tionality] person is neutral." template in Turkish with the Turkish nationality and the [MASK] token. This change shows that the "Turkish" nationality surfaces positive sentiment with +0.16 score while the "Greek" nationality surfaces negative sentiment with -0.17 score. Then, we compare these changes between across different nationalities and templates and evaluate if there is consisten negative bias towards specific nationalities.
Context Positivity
# of Sentences
Relative Sentiment
Nationality
Syrian
0.55
43k
-0.20
Victnamese
0.57
39k
0.03
Turk
0.57
7k
-0.48
Isracli
0,58
88k
0.04
Afghan
0,60
24k
0.06
Tranian
0,61
56k
-0.24
Japanese
0,61
348k
-0.09
Ukrainian
0.62
70k
0.03
German
0,63
593k
-0.1
Chinese
0.63
359k
-0.25
Arab
0.64
106k
-0.38
Ethiopian
0.64
17k
-0.33
Polish
0.65
148k
-0.15
Pakistani
0.65
34K
0.09
Korean
0.65
118k
0.15
Mexican
0.66
122k
-0.12
Indonesian
0.66
34k
-0.44
Moroccan
0.66
13k
-0.02
To surface sentiment change, utilize the three different sources of minimal pairs presented in $3.2: one coming from template pairs we curated and two coming from examples in mC4 (Raffel al., 2022) and Wikipedia corpora for six languages. Addi- tionally, we also modify and use the previously proposed EEC templates (Kiritchenko and Moham- mad, 2018) for English to show robustness of our approach to different template sources.
Greek
0.67
282k
0.00
Armenian
0.67
41k
0.13
African
0.67
304k
Irish
0.68
214k
0.02
Nigerian
0.68
0.24
Asian
0.69
188k
-0.11
Argentinian
0.70
5k
0.13
Indian
0.70
417k
Italian
0.71
285k
Filipino
0.72
Brazilian
0.72
American
0.72
1554k
0.08
5 Results
Table 3: "Context positivity" of nationality in the training corpus correlated with the trained model's bias measured by LABDet's "relative sentiment" in English (r = .5).. Context positivity represents the average positive sentiment score of sentences (i.e., con- texts) including each nationality in the pretraining data, as evaluated by RoBERTapaa finetuned on SST-2, Rel- ative sentiment is bias detection results obtained from LABDet (i.c.. the PLM is assessed without accessing the pretraining data). "# of Sentences" corresponds to the number of sentences in the pretraining data.
Experimental Setup: We evaluate LABDet using six different monolingual language models, all in the base size, with cased versions where available. Arabic PLM: ArabicBERT (Safaya et al., 2020), English PLM: BERT (Devlin et al., 2019), French PLM: CanemBERT (Martin et al., 2020), Dutch PL.:: BERTje (de Vries et al., 2019), and Turkish PLM: BERTurk (Schweter, 2020).
German PLM: bert-base-german-cone
For sentiment training, we use SVM and MLP classifiers. Next, we quantify bias using both a template-basedt based approach (ours and EEC -only for English-) and a corpus-base based approach (mC4 and Wikipedia) via sentiment surfacing.
cal and political context. For the first and second analyses, we employ the SVM classifier and our template-based approach. However, to demonstrate the robustness of our findings, we compare our re- sults from different approaches (template vs. cor- pus), sources (mC4 vs. Wikipedia), and classifiers (SVM vs. MLP) in the third analysis. We use Pear- son's 's to measure the strength of the correlation between positive sentiment scores of nationalities obtained from different sources.
We propose three distinct analyses. First, compare the bias extracted via LABDet with the bias of English BERT base pretraining data. This evaluation helps to assess the effectiveness of our method and explore the connection between pre- training data to PL. behavior. In the second anal- ysis, we show the relative sentiment change for each nationality across six languages. We conduct qualitative analysis of these results and exam- ine their link to real- world bias within the histori-
5.1 Pretraining Data Bias
We demonstrate the effectiveness of LABDet for detecting and quantifying bias by evaluating its per- formance on bias present in the pretraining data, a
Phttps: /wwww deepset. ai

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novel contribution compared to prior work. This ap- proach allows us to obtain evidence for a causal re- lationship between pretraining data and model bias. Specifically, we analyze the context positivity of different nationalities in the English BERT pretrain- ing data (i.c., English Wikipedia and CooksCorpus (Zhu et al., 2015)) by extracting all sentences con- taining a nationality/ethniictyy from set. We then measure the context positivity by calculating the average positive sentiment score of sentences for each nationality. We use RoBERTagase (Liu et al., 2019) finetuned with SST2 (Socher et al., 2013), for sentiment analysis. We eliminate nationality bias in the sentiment analysis model by replacing each nationality with mask token in the pretrain- ing data. For a more confident analysis, we also increase the number of nationalities from 15 to 30 for this part.
with conflict and tension have lower context posi- tivity scores. Nationalities such as Syrian, Afghan, Israeli, and Iranian are associated with conflict and violence in the past decades. Similarly, Vietnamese has one of the lowest context positivity scores most likely reflecting the bulk of content related with the Vietnam War. That Japanese and German have lower context positivity scores may seem puzzling at first; yet, this is likely due to the historical con- text of World War and their portrayal in the pre- training data.
To verify the effectiveness of LABDet, we com- pute the correlation between the context positivity scores of the pretraining data and the relative sen- timent scores from our method using Pearson's 's T. We observe a significant correlation with an r score of 0.59 (< 0.01 p-value). This indicates that LABDet is able to detect bias in PLMs with high correlation to the bias present in the pretraining data. We also observe significant linear correla- tion using different approaches such as templates and corpus examples SVM and MLP classifiers. This shows LeB robustness.
We present pretraining data bias and relative sen- timent scores with our method for all nationalities in Table 3. We observe meaningful patterns in the context positivity scores of English BERT's pre- training data. The connection with the English language, historical developments, and content pro- duction can explain why some countries receive higher context positivity score while others remain on the lower end.
5.2 Linkage to Real-world Biases
We compare the bias of six monolingual PLMs identified by LABDet to real-world bias, consult- ing the political science literature. We report the relative sentiment score changes with reference to neutral examples where [Nationality] tags are replaced by mask token. Using the Wilcoxon signed-rank test, we determine which nationalities have consistently lower, higher, or similar predic- tions compared to the neutral examples. Our results are presented in Figure with the relative senti- ment change, bias direction (red: negative, black: no bias, green: positive), and confidence intervals. Our findings indicate that all monolingual PLMs exhibit bias in favor of (i.c., green) and against (i.e., red) certain nationalities. In each model, we are able to get diverging relative sentiment scores just due to the change in the nationality mentioned across our templates. Some nationalities consis- tently rank low on different PLMs. For instance, Syrian, Israeli, and Afghan rank on the lower end of most PLMs in our analysis. Similarly, Ukrainian ranks fairly low in the European language PLMs. On the other hand, some nationalities, such as American and Indian rank consistently high in rela- tive sentiment scores. Apart from consistent rank- ings across PLMs, there are three context-specifit
For instance, American has the highest context positivity, which could be attributed to the fact that English is widely spoken in the country and the ma- jority of the content in the pretraining data is pro- duced by Americans (Callahan and Herring, 2011). Similarly, nationalities with large numbers of En- glish speakers like Indian and Nigerian (and by extension, Asian and African) as well as Irish also have high context positivity scores, which could be explained by the fact that these countries also produce content in English. For example, most ac- tive editors of English Wikipedia are from United States of America (21K), followed by United King- dom (6K) and India (4K). Indeed, among the countries with highest context positivity in Table 3, all except one are among the top content producer countries (Philippines IK; Italy 950; Brazil 880). 5
On the negative end of context positive score, observe that groups that have minority status in En- glish speaking countries or those that are associated
We analyze the 20/03/2018 dump of Wikipedia. *More than 75% BERT's pretraining data and over 90% of sentences containing nationality information are sourced from English Wikipedia.
httpps: //stats.tttttt/// wikimedia.aoo /

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Arabic PLM
The Dutch
English
Areenaan
Aighan
Turk
Geman
Moroccen
Chiiss
Afrean
Innell
Serepalese
Syrtn
Garmer
hatorslity
Morossen
higerien
Ukraiiian
Turc
Bajen
lark
teae]
Moroccar
Arob
Jesb
Ulnamer
loghan
indian
Israel
Gresk
Gomman
Armanar
Dutth
Cerec
Indlar
Synan
Nigerar
-10
-0.5
0.0
0.
-1.9
005
0.8
0.5
1,0
-10
-0.5
0.0
0.5
1.
French
Ukranian
Uvairian
Armentar
Sytian
Atrzer
Gerann
(sracli
Palish
Monocosn
Ameetinn
Clensse
lark
Afican
Mghan
Pdlsh
Grnec
Moroccar
Cermer
Aigher
franch
Istell
Americnn
Greek
Settion
Semtir
Creek
Indian
Indor
Monocca
Turt
Sanageless
-1.0
-2.5
0.0
0.5
1.0
-1.0
-0.5
0,5
2.5
1.0
-1.0
-2.5
0.0
0.5
1.0
Relattve Sentiment
Rebtive Sertmmnnt
Rebtivve Sentiment
Figure 2: Relative sentiment score computed by LABDet for each nationality in six monolingual PLMs. Error bars indicate 95% confidence interval. Colors in each PLM indicate negative (red), neutral (black), and positive (green) groups. Negative and positive groups are statistically different from the neutral examples, i.e., from examples with the [MASK] token (Wilcoxon signed-rank test, < le p-valuc).
patterns we can observe from the relative sentiment scores:
discrimination against these minorities their re- spective countries (Thijssen et al., 2021; Silberman et al., 2007). The fact that English has official lan- guage status in India and Nigeria, and French in Senegal might also be contributing factor.
First and foremost, immigrant/minority popula- tions are important predictors of relative sentiment scores. This manifests in two opposing trends. On the one hand, immigrant populations such as Syr- ians in Turkey (Getmansky et al., 2018; Alakoc al., 2021) and the European countries (Poushter, 2016; Secen, 2022), Ukrainians (Düvell, 2007) in European countries (note that the PLMs were trained prior to the recent refugee wave), and In- donesians/Moroccanss in the Netherlands are known to be stigmatized (Solodoch, 2021). In accordance with that, these nationalities have negative and some of the lowest relative sentiment scores in the corresponding Turkish, Dutch/German/Freechh and Dutch PLMs, respectively. Similarly, minorities such as Arab, Syrian, Chinese, and Mexican rank lower in the English PLM.
These two trends regarding minorities are likely driven by the history and size of minorities in these countries. While there is bias against newer and rel- atively smaller minorities, the older and larger mi- norities who likely produce content receive higher relative sentiment scores. Reflecting these trends, the German PLM is case in point. The nationali- ties that rank among the lowest in the German PLM are either recent immigrants (Syrian) smaller mi- nority groups (Ukrainian before the recent refugee wave, Moroccan, and Nigerian). On the opposite end, Turk ranks among the highest in the German PLM. As Turkish immigrants constitute the largest and one of the oldest immigrant populations Ger- many (Destatis, 2023), it is likely that the content they produce leads to a positive bias toward Turks.
On the other hand, while there is some evidence of bias against minorities, it seems that large mi- nority populations who reside in a country and/or produce language content that might have made it into the pretraining data may be helping to miti- gate some of the impacts of that bias. For example, Moroccan and Senegalese are associated with high relative sentiment scores in the French PLM. The same is true for Indian and Nigerian the English PLM. This is despite the evidence of significant
Second, negative bias seems to stem not just from attitudes toward minorities but also from geopolities and conflict. This might be in the form of geopolitical tension between country where the language of the model is predominantly spo- ken and country that we consider as a nation- ality. This is consistent with the evidence in the literature that geopolitical tensions stoke discrim-

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inatory attitudes (Saavedra, 2021). For example. tensions between the US and countries like Iran and China are likely driving lower scores for Ira- nian and Chinese in the English PLM (Lee, 2022; Sadeghi, 2016). Similarly, regional tensions in the Middle East are reflected in Arabic and Turkish PLMs where Israelis ranked among the lowest in terms of relative sentiment scores (Kosebalaban, 2010; Robbins, 2022).
As the examination of these three patterns (i.e.. minorities, geopolities, and historical relations) demonstrates, the relative sentiment scores in Fig- ure 2 highlight the importance of considering histor- ical and contemporary real-world context in analyz- ing the biases present in PLMs. Understanding the real-world biases provides valuable insights into the underlying factors that contribute to the biases in PLMs. Furthermore, this illustrates how cultural and historical ties between nations can have a last- ing impact on language usage, which is evident in the pretraining data, subsequently reflected in PLMs.
Geopolitical tensions and a conflict environment can also affect attitudes through negative news sto- ries, even when there is no direct conflict between countries. The fact that nationalities of conflict- ridden countries such as Syrian, Israeli, and Afghan have consistently negative sentiment scores shows how political conflict may affect attitudes toward groups in different parts of the world.
5.3 Robustness Evaluation
Robustness is a crucial aspect of bias detection in PLMs, and many existing methods have limitations in this regard (Delobelle et al., 2022). We com- pare robustness of LABDet to different setups by assessing the similarity in predictions via Pearson's (< 0.01 p-value) across languages. Classifier:
Finally, historical affinity and rivalry seem to play a significant role. Historical allies tend to have higher relative sentiment scores, as seen in the case of Americans that are ranked high in Dutch, French, and Arabic PLMs. Yet, historical rivals tend to be ranked rather negatively. The fact that German has negative relative sentiment scores in the three other European PLMs (Dutch, English, and French) is likely related to Germany's role the world wars (Reeve, 2017). Similarly, Ukrainian consistently ranking lower in the European PLMs might be a by-product of the Cold War context where Ukraine was part of the USSR in rivalry with the Western Bloc.
We compare SVM and MLP classifiers on six language models and four template sources. For each experiment, we observe a significant cor- relation with an average r of 0.94.
Template Source: To demonstrate that our results are not specific the design of our templates with neutral adjectives, we compare them to modified EEC (Kiritchenko and Mohammad, 2018) tem- plates with positive and negative adjectives (see Table 4). As EEC templates are in English, we only compare English PLMs (but by extending to four BERT and two RoBERTa variants) and two different classifiers. We observe significant linear correlation for each setup with an average 0.83 Template vs. Corpus Examples: We compare our template approach to mC4 examples. For PLMs in six languages and two classifiers, we observe a sig- nificant correlation with an average 0.89 except for Arabic where there is significant difference between corpus examples and templates.
Examining the relative sentiment scores in the Turkish PLM is also helpful to explore how his- torical relations shape both negative and positive biases. The historical negative attitudes between Turkey and Armenia (Phillips, David L., 2013) are reflected in the chart Armenian is ranked lowest in the Turkish PLM. This sentiment likely arises from the long-standing tension and conflict between the two nations going back to World War I. On the other hand, Korean has the most positive sentiment score in the Turkish PLMM a result that may seem puzzling at first, considering the geo- graphical and political distance between the two countries. However, digging into the historical con- nection between the two countries helps explain this score, Turkey provided military support dur- ing the Korean War (Lippe, 2000), which evolved into an affinity between the two nations that has even become a subject of popular culture through literature and movies (Betul, 2017).
Corpus Source: We investigate the importance of the corpus source by comparing Wikipedia and mC4 examples in six monolingual PLMs and two classifiers. We observe significant correlations for each combination, with an average 0.98
Conclusion
Our bias probing method, LABDet, allows for the analysis of intrinsic bias in monolingual PLMs and is easily adaptable to various languages. Through

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rigorous testing and qualitative analysis, have demonstrated the effectiveness of LABDet, such as finding a strong correlation between bias in the pre- training data of English BERT and our results. We also identified consistent patterns of bias towards minority groups or nationalities associated with conflict and tension across different languages. Ad- ditionally, we found that large minority groups who produce content in the target language tend to have more positive sentiment, such as Turks in German PLMs and Senegalese/Morocaans in French PLMs. Finally, we show that our findings are statistically consistent across template and corpus examples, different sources, and languages.
acknowledge that our approach may not fully cap- ture all forms of bias that may be present in PLMs. Therefore, it is beneficial to use our method con- junction with other techniques or metrics to gain a more comprehensive understanding of the biases present in PLMs and to make informed decisions about the ethical use of these models.
References
Ekin Akyurek, Tolga Bolukbasi, Frederick Liu, Bin- hin Xiong, lan Tenney, Jacob Andreas, and Kelvin Guu. 2022. Towards tracing knowledge in language models back to the training data. Findings of the Association for Computational Linguistics: EMNLP 2022, pages 2429 2446, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Acknowledgements
This work was partially funded by Deutsche Forschungsgemeinsccaat (project SCHU 2246/14- 1) and the European Research Council (grant #740516).
Burcu Pinar Alakoc, Gulay Ugur Goksel, and Alan Zarychta. 2021. Political Discourse and Public Atti- tudes toward Syrian Refugees in Turkey.
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Limitations
One limitation of LABDet is related to the design of templates. It is possible that some templates may generate ungrammatical examples in differ- ent languages, particularly due to gender or article agreement. While we have shown that LABDet is robust to these changes through the use of dif- ferent templates and corpus examples, is still important to consider this limitation when utilizing LABDet. We recommend comparing the results obtained from different templates to gain more comprehensive understanding of the bias present in PLMs.
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Ethics Statement
The ethical implications of social biases in mono- lingual PLMs are significant as these models are increasingly being used in a wide range of tasks such as text classification (Wang et al., 2018), ex- tractive text summarization (Liu and Lapata, 2019), and non-autoregressiive text generation (Su et al., 2021). The biases present in these models have the potential to amplify existing societal inequali- ties, particularly for marginalized groups. In this study, we propose LABDet, a robust method for quantifying and measuring bias in PLMs across different languages. For nationality as a case study, we aim to demonstrate the applicability and effec- tiveness of our method. However, it is important to
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A Appendix

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Modified EEC Templates
1. talked this [Nationality] person yesterday.
2. This [Nationality] person found himself in [Situation] situation.
3. This [Nationality] person made feel [Stute].
4. The conversation
this [Nationality] person was [Situation].
5. This [Nationality] person goes the school in neighborhond.
Templates
This [Nationality] person told us about the recent [Situation] events.
saw this [Nationality] person the market. made this (Nationality] person feel [State].
The [Nationality] feels [State].
This [Nationality] person has two children.
The situation makes [Nationality] feel [State].
State Words
angry. anxious, ecstatic, depressed, annoyed, discouraged, excited, devastated, enraged, fearful, glad, disappointed, furious, scared, happy, miserable, irritated, terrified, relieved, sad annoying, dreadful, amazing, depressing, displeasing, horrible, funny, gloomy, irritating, shocking, great, grim, outrageous, terrifying, hilarious, hearthreaking, vexing, threatening, wonderful, serious
Situation Words
Table 4: Modified EEC (Kiritchenko and Mohammad, 2018) templates for the bias detection of PLMs.


Reviews:
[{"review_id": "iqc2cwtU9X", "reviewer": "Reviewer_qjcz", "summary": "", "questions": "a) Since the models are only trained on positive and negative samples (no neutral ones), does this training induce a bias in itself? I don't consider this a problem, since there seem to be clear tendencies of the model to associate a nationality with either positive or negative sentiment, but it will likely still push the model away from the (correct) neutral predictions. Or is this even intentional, to force the model to show its bias?", "limitations": "", "rating": 3, "confidence": 4, "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 the reviewer for the valuable comments.\n\n> Using two-way sentiment classification instead of three-way classification:\n\nTwo-way sentiment classifiers do not inherently introduce bias, as they have the\ncapability to predict neutral inputs by giving approximately 0.5 probability\nscores for both positive and negative classes. We further analyzed this on\n10,000 corpus examples in the English data with our sentiment analysis model. Out of these, 3,078 examples have\nneutral scores, falling within the range of 0.4<P(y=positive|x)<0.6. Therefore,\nwe can say that our two-way sentiment classifier is able to make neutral\npredictions. We made an intentional decision in favor of two-way classifiers,\nsince they would have better performance in detecting positive and negative ends\nof the sentiment spectrum\n(which is more important for our work) than three-way classifiers while\nunderstanding neutral instances as well.", "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) Simple, but effective method\n\nb) Generalisable to any language with existing pre-trained Language Models, as well as other types of bias\n\nc) Well-written paper", "reasons_to_reject": "a) Somewhat limited scope:\nEven though the method is general enough to be applied to any kind of bias, it is only evaluated on nationality bias. This might be a bit limited for a full paper.\n\nb) Somewhat anecdotal evaluation:\nThe paper shows many examples where the detected biases are consistent with results from political sciences - enough to convince me that the results are valid. However, for a fully reliable evaluation, it might be better to select one or two (source) languages or (target) nationalities and have all of the detected, associated biases evaluated by a domain expert (or, if possible multiple experts). I don't see this as absolutely necessary, but as something that would make the results even more convincing.", "questions_to_authors": "a) Since the models are only trained on positive and negative samples (no neutral ones), does this training induce a bias in itself? I don't consider this a problem, since there seem to be clear tendencies of the model to associate a nationality with either positive or negative sentiment, but it will likely still push the model away from the (correct) neutral predictions. Or is this even intentional, to force the model to show its bias?", "paper_topic_and_main_contributions": "The paper proposes a method to detect the nationality bias present in a pre-trained Language Model. To this end, the LM is fine-tuned for sentiment analysis on a corpus that is designed not to have any nationality bias (by manually creating the samples from templates) and then tested on neutral sentences containing nationality information. The method uncovers biases in LMs that are consistent with expectations from political science.", "excitement": 3, "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": "VxGkNBV2Mg", "reviewer": "Reviewer_TUSA", "summary": "", "questions": "A. In Table 2, I am curious about why use different corpus templates for various languages instead of employing the same templates as shown in Table 1.", "limitations": "", "rating": 3, "confidence": 3, "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": "We thank the reviewer for the valuable comments.\n\n> “In Table 2, I am curious about why use different corpus templates for various\nlanguages instead of employing the same templates as shown in Table 1.”\n\nCorpus examples in Table 2 are extracted from their monolingual Wikipedia corpus\nby querying nationality terms. Therefore, we cannot select the same templates\nfor various languages.\n\nFor all experiments, we only employ templates from Table 1. We only compare\ncorpus examples to templates in \"5.3. Robustness Evaluation - Template vs.\nCorpus Examples\" to show our method’s robustness to the use of minimal pair\ntemplates other than those that we designed ourselves. \n\n> “Lack of implementation details”\n\nWe use the same implementation setup for all languages, described in L289-L296\nfor training, L297-L314 for sentiment surfacing/inference with the specific PLMs\nfor each language (L329-L333). We will release the reproducible codebase in the\ncamera ready version.\n\n> “The experiments are limited to only [...] one dataset - SST2.”\n\nWe use SST2 for the pretraining data analysis in 5.1 while we mostly use our\ntemplates for sentiment training in the rest of the 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": "1. The paper is clear and well-structured.\n2. It is interesting to explore the bias towards minority groups or nationalities associated with conflict and tension across different languages and show some interesting findings, like \"large minority groups who produce content in the target language tend to have more positive sentiment, such as Turks in German PLMs and Senegalese/Moroccans in French PLMs\".", "reasons_to_reject": "1. Limited task and dataset. The experiments are limited to only one task - sentiment classification - and one dataset - SST2. To gain deeper insights, it would be better to explore the effects on different tasks or utilize multiple datasets for the same task.\n2. Lack of implementation details. For example, the details of training classifiers of the multilingual models are missing.", "questions_to_authors": "A. In Table 2, I am curious about why use different corpus templates for various languages instead of employing the same templates as shown in Table 1.", "paper_topic_and_main_contributions": "This paper proposes a language-agnostic bias detection method for nationality in monolingual PLMs in Arabic, Dutch, English, French, German, and Turkish. First, they use templates containing positive/negative adjectives without any nationality information to train a sentiment classifier on top of PLMs. Then they use templates and corpus examples with a nationality slot, e.g., This [Nationality] person is neutral, to obtain the sentiment score for different nationalities. By subtracting absolute sentiment scores from the sentiment score of a neutral example without nationality, e.g., This [MASK] person is neutral, the relative sentiment score indicates the effect of nationality on the sentiment.", "excitement": 3, "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": {}, "extra_text": {}}, {"review_id": "Z0xDcGvycN", "reviewer": "Reviewer_W1HN", "summary": "", "questions": "", "limitations": "", "rating": 4, "confidence": 4, "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 the reviewer for the valuable comments and will update our paper\naccordingly.", "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": "This paper makes progress in measuring social bias in language models that is less vulnerable to small perturbations in input. They also point out the importance of correlating the results with bias in the training dataset and collaborating with political scientists to do that.", "reasons_to_reject": "As the authors mention, there are limitations involving the templates used. Moreover, they also point out that there may be forms of bias that are not captured by this method. It would be good to mention somewhere that even if this method yields the result that it cannot detect bias in the model, that does not mean that the model is unbiased.", "questions_to_authors": "", "paper_topic_and_main_contributions": "The authors propose LABDet, a language-agnostic method of measuring social bias in pretrained language models. They point out that prior methods (namely, evaluating perplexity over minimal pairs) are sensitive to minor perturbations in input. They mitigate this by training a sentiment classifier by adding a layer to the language model, and then computing the difference in resulting sentiment scores for neutral-sentiment minimal pairs. They show their results on a dataset for nationality, analyze the correlation with sentiment in the language model's training data, and measure variability across languages to understand the robustness of their method.", "excitement": 4, "reproducibility": 5, "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": {}}]

Author rebuttal:

Meta-review: The authors proposed a method to detect the nationality bias present in a pre-trained Language Model. They fine-tuned LM on a sentiment corpus that is designed based on specific templates. Finally, they showed the bias in LMs while testing on a corpus containing nationality information. 

Overall, reviewers agree that the work is sound, and contains interesting findings on bias in language models, though the application scope is limited (Reviewer qjcz and TUSA).
Decision: Accept-Findings