Towards Region-aware Bias Evaluation Metrics (2024)

Angana Borah¹ Aparna Garimella² Rada Mihalcea¹
¹University of Michigan, Ann Arbor, USA,²Adobe Research, India

anganab@umich.edu garimell@adobe.com mihalcea@umich.edu

Abstract

When exposed to human-generated data, language models are known to learn and amplify societal biases. While previous works introduced benchmarks that can be used to assess the bias in these models, they rely on assumptions that may not be universally true. For instance, a gender bias dimension commonly used by these metrics is that of family–career, but this may not be the only common bias in certain regions of the world. In this paper, we identify topical differences in gender bias across different regions and propose a region-aware bottom-up approach for bias assessment. Our proposed approach uses gender-aligned topics for a given region and identifies gender bias dimensions in the form of topic pairs that are likely to capture gender societal biases. Several of our proposed bias topic pairs are on par with human perception of gender biases in these regions in comparison to the existing ones, and we also identify new pairs that are more aligned than the existing ones. In addition, we use our region-aware bias topic pairs in a Word Embedding Association Test (WEAT)-based evaluation metric to test for gender biases across different regions in different data domains. We also find that LLMs have a higher alignment to bias pairs for highly-represented regions showing the importance of region-aware bias evaluation metric.

Towards Region-aware Bias Evaluation Metrics

Angana Borah¹ Aparna Garimella² Rada Mihalcea¹¹University of Michigan, Ann Arbor, USA,²Adobe Research, Indiaanganab@umich.edu garimell@adobe.com mihalcea@umich.edu

1 Introduction

Human bias refers to the tendency of prejudice or preference towards a certain group or an individual and can reflect social stereotypes with respect to gender, age, race, religion, and so on.

Bias in machine learning refers to prior information which is a necessary prerequisite for intelligence Bishop (2006). However, biases can be problematic when prior information is derived from harmful precedents like prejudices and social stereotypes. Early work in detecting biases includes the Word Embedding Association Test (WEAT) Caliskan etal. (2017) and the Sentence Encoder Association Test (SEAT) May etal. (2019). WEAT is inspired by the Implicit Association Test (IAT) Greenwald etal. (1998) in psychology, which gauges people’s propensity to unconsciously link particular characteristics—like family versus career—with specific target groups—like female (F) versus male (M).WEAT measures the distances between target and attribute word sets in word embeddings using dimensions¹¹1We use ‘topic pairs’ and ‘topic dimensions’ interchangeably. similar to those used in IAT.

Biases toward or against a group can vary across different regions due to the influence of an individual’s culture and demographics Grimm and Church (1999); Kiritchenko and Mohammad (2018a); Garimella etal. (2022). Psychological studies and experiments that demonstrate human stereotypes vary by continental regions Damann etal. (2023); Blog (2017) and even larger concepts like western and eastern worlds Markus and Kitayama (2003); Jiang etal. (2019) serve as an inspiration for the use of regions to determine differences across cultures. However, existing bias evaluation metrics like WEAT and SEAT follow a “one-size-fits-all” approach to detect biases across different regions.As biases can be very diverse depending on the demographic lens, a fixed or a small set of dimensions (such as family–career, math–arts) may not be able to cover all the possible biases in the society.In this paper, we address two main research questions about gender bias: (1) Is it possible to use current NLP techniques to automatically identify gender bias characteristics (such as family, career) specific to various regions? (2) How do these gender dimensions compare to the current generic dimensions included in WEAT/SEAT?

Our paper makes four main contributions:

1.
An automatic method to uncover gender bias dimensions in various regions that uses (a) topic modeling to identify dominant topics aligning with the F/M groups for different regions, and (b) an embedding-based approach to identify F-M topic pairs for different regions that can be viewed as gender bias dimensions in those regions.
2.
An IAT-style test to assess our predicted gender bias dimensions with human subjects. To the best of our knowledge, this is the first study to use a data-driven, bottom-up method to evaluate bias dimensions across regional boundaries.
3.
A WEAT-based evaluation setup using our region-aware topic pairs to evaluate gender biases in different data domains (Reddit and UN General Debates) across regions.
4.
An analysis of how well our predicted bias dimensions align with those of custom LLMs. We consider several LLMs that include open-source models like Llama-3-8b and Mistral-7b-Instruct; as well as closed-source models such as GPT-4, Gemini-Pro and Claude-3-Sonnet.

2 Data

We use GeoWAC Dunn and Adams (2020a), a geographically balanced corpus that consists of web pages from Common Crawl. Language samples are geo-located using country-specific domains, such as an $.in$ domain suggesting Indian origin Dunn and Adams (2020b). The GeoWAC’s English corpus spans 150 countries. We select the top three countries with the most examples per region: Asia, Africa, Europe, North America, and Oceania as in Garimella etal. (2022). We randomly choose 282,000 examples (after pre-processing) for each region, with 94,000 examples belonging to each country within the regions. Dataset details are included in AppendixA.

3 Variations in Gender Bias Tests Across Regions

We start by investigating the differences in existing gender bias tests across different regions using WEAT. WEAT takes in target words such as male names and female names, to indicate a specific group, and attribute words that can be associated with the target words, such as math and art. It computes bias by finding the cosine distance between the embeddings of the target and attribute words.We compute WEAT scores using word2vec embeddings Mikolov etal. (2013) trained on the five regions separately. Table1 shows the region-wise scores for the three gender tests in WEAT.

Target words - Attribute words	Region	WEAT
	Africa	1.798
	Asia	1.508
career vs family - Male names vs Female names	North America	1.885
	Europe	1.610
	Oceania	1.727
	Africa	1.429
	Asia	1.187
Math vs Arts - Male terms vs Female terms	North America	0.703
	Europe	0.334
	Oceania	1.158
	Africa	1.247
	Asia	0.330
Science vs Arts - Male terms vs Female terms	North America	0.036
	Europe	-0.655
	Oceania	0.725

Although we see a positive bias for most gender bias dimensions, the scores vary across regions.For example, the highest scoring regions vary for the target words-attribute words groups. For family–career dimension, North America shows the highest bias, however for math–arts and math–science dimensions, Africa shows the highest bias.Europe has a negative bias on science–arts (indicating a stronger F-science and M-arts association).

These results provide preliminary support to our hypothesis that gender bias dimensions vary across regions, thus propelling a need to come up with further bias measurement dimensions to better capture gender biases in these regions in addition to the existing generic ones in WEAT.

4 A Method to Automatically Detect Bias Dimensions Across Regions

Building upon our WEAT findings, we propose a two-stage approach to automatically detect region-aware bias dimensions that likely capture the biases in specific regions in a bottom-up manner.In the first stage, we utilize topic modeling to identify prominent topics in each region. In the second stage, we use an embedding-based approach to find pairs of topics among those identified in the first stage that are likely to represent prominent gender bias dimensions in each region.Fig1 shows the pipeline of our methodology.

4.1 Identifying Region-wise Bias Topics

We use topic modeling to identify dominant topics in the male and female examples in each region.

We first build F(emale)- and M(ale)-aligned datasets using the examples from GeoWAC for each region.We use the 52 pairs of gender-defined words that are non-stereotypically F/M (e.g., wife, brother, see AppendixG) from Bolukbasi etal. (2016), and find examples that contain these words.These datasets are used to find gender-aligned topics from GeoWAC.The dataset statisticsare specified in Table6 in AppendixB.

We then use topic modeling to identify dominant topics in the male and female examples in each region.We use Bertopic Grootendorst (2022), which identifies an optimal number of topics $n$ for a given dataset (see AppendixL.1 for implementation details). We further refine the resulting topics using Llama2 Touvron etal. (2023) to label and better understand the topic clusters identified by Bertopic. The prompting mechanism for Llama2 is provided in AppendixH.

We next compute the alignment of the topics to either of the F/M groups. We first compute the topic distribution of a data point, which gives the probability $p_{it}$ of an example $i$ belonging to each topic $t$ . For a topic $t$ , we take n examples that dominantly belong to topic $t$ : $i_{1},i_{2},....,i_{n}$ . If m out of n data points belong to the F group in the F-M dataset, and the other (n - m) belongs to the M group, we compute the average of topic probabilities for both groups separately: $p_{Ft}=\frac{(p_{i_{1}t}+p_{i_{2}t}+......+p_{i_{m}t})}{m}$ and $p_{Mt}=\frac{(p_{i_{m+1}t}+p_{i_{m+2}t}+......+p_{i_{n}t})}{(n-m)}$ , where $p_{Ft}$ and $p_{Mt}$ refer to the average probability by which a topic dominantly belongs to the F and M groups respectively. If $p_{Ft}>p_{Mt}$ , we say the topic is a bias topic that aligns with the F group and vice-versa.

4.2 Finding Topic Pairs as Region-wise Bias Dimension Indicators

We use an embedding-based approach to identify F-M topic pairs from the pool of topics identified in the previous stage, to generate topic pairs (bias dimensions) that are comparable to IAT/WEAT pairs.

We use BERT-large (stsb-bert-large) from SpaCy’s Honnibal and Montani (2017) sentencebert library to extract contextual embeddings for topic words extracted from the GeoWAC dataset for each region.For a topic $t$ consisting of topic words $w_{1},..w_{n}$ , the topic embedding is given by the average of embeddings of the top ten topic words in that topic.

We identify topic pairs from the embeddings taking inspiration from Bolukbasi etal. (2016): let the embeddings of the words $she$ and $he$ be $E_{she}$ and $E_{he}$ respectively. The embedding of a topic $t_{i}$ be $E_{t_{i}}$ . A female topic $F_{t_{i}}$ and a male topic $M_{t_{j}}$ are a topic pair if: $cos(E_{F_{t_{i}}},E_{she})\sim\ cos(E_{M_{t_{j}}},E_{he})$ and/or $cos(E_{F_{t_{i}}},E_{he})\sim\ cos(E_{M_{t_{j}}},E_{she})$ , where $cos(i,j)$ refers to the cosine similarity between embeddings $i$ and $j$ , given by $cos(i,j)=\frac{i,j}{||i||||j||}$ . The threshold for the difference between the cosine similarities we consider for two topics to be a pair is 0.01, i.e., two topics $(t1,t2)$ are considered a pair if the difference of cosine similarities cos( $t1$ , $she$ )/cos( $t1$ , $he$ ) and cos( $t2$ , $he$ )/cos( $t2$ , $she$ ) respectively is $<0.01$ . We manually choose 0.01 since differences close to 0.01 are almost $=0$ .

Towards Region-aware Bias Evaluation Metrics (2)

4.3 Human Validation Setup

We design an IAT-style test to validate our topic pairs with annotators from different regions. We recruit six annotators from each region controlled by gender (three female and three male). In addition to our topics, we also test for existing WEAT dimensions relating to gender, namely family–career, math–arts, and science–arts. For each region, we validate all the region-aware topic pairs using the assistance of our annotators.

As done in IAT, to verify a topic pair, we show the topic names and male/female faces to our annotators along with a set of guidelines. As shown in Fig2, each topic pair test form contains two tasks.First, the annotators have to press one key for a female face $f$ and a female topic $T_{f}$ and another key for a male face $m$ with a male topic $T_{m}$ , timing responses as $r_{1}$ and $r_{2}$ .In the reverse task, they pair $T_{m}$ with $f$ and $T_{f}$ with $m$ , timing these as $r_{3}$ and $r_{4}$ . We average $r_{1}$ and $r_{2}$ for the ‘un-reversed’ case and $r_{3}$ and $r_{4}$ for the ‘reversed’ case. The annotators’ implicit association of a gender to a topic may influence their response time. A lower response time suggests easier recollection of the guidelines and potential implicit gender-topic associations, and thus lower bias with respect to these topics. We also varied the test order for different annotators to avoid initial pairing bias.We conduct the survey with six annotators each from Africa, Asia, Europe, and North America, also including a family-career topic pair, a standard WEAT bias dimension. We provide screenshots of our annotation framework in AppendixM.

4.4 Results: Bias Dimensions across Regions

4.4.1 Region-wise Bias Topics

Table2 displays the top topics based on $u_{mass}$ Mimno etal. (2011) coherence for each region.

Region	Female	Male
Africa	Credit cards and finances, Royalty and Media, Trading strategies and market analysis, Dating and relationships guides, Parenting and family relationships	Fashion and Lifestyle, Male enhancement and sexual health, Nollywood actresses and movies, Nigerian politics and government, Essay writing and research
Asia	Hobbies and Interests, Healthy eating habits for children, Social media platforms, Royal wedding plans, Online Dating and Chatting	DC comic characters, Mobile Application, Phillippine Politics and Government, Sports and Soccer, Career
Europe	Pets and animal care, Fashion and Style, Education, Obituaries and Genealogy, Luxury sailing	Political developments in Northern Ireland, Christian Theology and Practice, Crime and murder investigation, EU Referendum and Ministerial Positions, Criminal Justice System
North America	Pets, Cooking: culinary delights and chef recipes, Fashion and style, Family dynamics and relationships, Reading and fiction	Civil War and history, Middle East conflict and political tensions, Movies and filmmaking, Political leadership and party dynamics in Bermuda, Rock Music and songwriting
Oceania	Cooking and culinary delights, Romance, Weight loss and nutrition for women, Water travel experience, Woodworking plans and projects	Harry Potter adventures, Art and Photography, Superheroes and their Universes, Music recording and Artists, Football in Vanuatu

Several topics are exclusive to certain regions. Some topics like family and parenting; cooking; pets and animal care are common across some regions for F. Similarly we have movies; politics and government; sports; and music for M. Finally, there are differences between regions in terms of education, reading, and research (F-Europe, NA, and M-Africa), and fashion and lifestyle (F-Europe, NA, and M-Africa). Some other popular topics across regions are religion and spirituality, Christian theology in M; obituaries and genealogy, online dating, travel, and sailing in F (see AppendixD for a comprehensive list of topics). We provide an example of topic clusters in AppendixJ.

4.4.2 Region-wise Bias Dimensions

Table3 shows the top five topic pairs per region, chosen based on the $u_{mass}$ score from the top 10 topics each for F and M from the topic modeling scheme. As expected, topic pairs differ by region, and we also note new topic pairs that do not appear in the WEAT tests. Among the top ones, there are recurring topics in F such as dating and marriage, family and relationships, luxury sailing, and education, whereas in M, we have politics, religion, sports, and movies.These region-specific pairs may supplement generic tests to detect regional biases.

Region	F-M topic pair
Africa	Parenting and family relationships-Nollywood Actress and Movies (P1)
	Marriage and relationships - Sports and Football (P2)
	Womens’ lives and successes - Fashion and Lifestyle (P3)
	Music - Social Media (P4)
	Dating and relationships advice - Religious and Spiritual growth (P5)
Asia	Hotel royalty - Political leadership in India (P1)
	Healthy eating habits for children - Sports and Soccer (P2)
	Royal wedding plans - Social Media platforms for video sharing (P3)
	Royal wedding plans - Religious devotion and spirituality (P4)
	Marriage - Bollywood actors and films (P5)
Europe	Education - Music (P1)
	Comfortable hotels - Political decision and impact on society (P2)
	Luxury sailing - UK Government Taxation policies (P3)
	Obituaries and Genealogy - Christian Theology and Practice (P4)
	Fashion and style - Christian theology and practice (P5)
	Online Dating for Singles - Religion and Spirituality (P1)
North	Fashion and Style - Reproductive Health (P2)
America	Education and achievements - Reinsurance and capital markets (P3)
	Family dynamics and relationships - Nike shoes and fashion (P4)
	Reading and fiction - Cape Cod news (P5)
Oceania	Family relationships - Religious beliefs and figures (P1)
	Woodworking plans and projects - Music record and Artists (P2)
	Weight loss and nutrition for women - Building and designing boats (P3)
	Exercises for hormone development - Superheroes and their Universes (P4)
	Kids’ furniture and decor - Building and designing boats (P5)

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Towards Region-aware Bias Evaluation Metrics (4)

4.4.3 Unigram/Bigram Analysis

We find several topics that are common across regions. However, they may differ across cultures and may reveal varied perceptions of biases. Several topics also change associations to genders based on regions. For example, ‘fashion and lifestyle’ in Africa is associated with males, however, it is associated with females in Europe and North America. Several topics like ‘family and parenting’ are commonly associated with females across different regions while ‘politics’ is associated with males. To this end, we compute the top uni-grams and bi-grams for topic pairs that are common across regions in AppendixE.

4.4.4 Human Validation Results

Fig3 shows response times for top five topic pairs in each region for un-reversed and reversed scenarios. Larger time differences indicate more bias, suggesting that the pair could be a potential gender bias dimension for that region. If un-reversed time is lower, it suggests a stronger association of $T_{f}$ with the F group and $T_{m}$ with the M group. The family-career pair was also surveyed as a standard WEAT bias dimension. Please refer to Table3 for topic pair numbers (P1…P5) of each topic pair.

Region	F-M topic pair	Reddit	UN General
			Debates
	Parenting and family relationships-Nollywood Actress and Movies	0.500	0.979
	Marriage and relationships - Sports and Football	-0.051	0.224
Africa	Womens’ lives and successes - Fashion and Lifestyle	0.480	0.493
	Music - Social Media	1.894	1.721
	Dating and relationships advice - Religious and Spiritual growth	1.475	1.061
	Hotel royalty - Political leadership in India	1.365	1.768
	Healthy eating habits for children - Sports and Soccer	0.006	-0.068
Asia	Royal wedding plans - Social Media platforms for video sharing	1.05	1.393
	Royal wedding plans - Religious devotion and spirituality	1.183	1.335
	Marriage - Bollywood actors and films	1.543	0.918
	Education - Music	1.261	1.920
	Comfortable hotels - Political decision and impact on society	0.324	0.485
Europe	Luxury sailing - UK Government Taxation policies	1.232	1.558
	Obituaries and Genealogy - Christian Theology and Practice	0.001	-0.405
	Fashion and style - Christian theology and practice	1.730	1.028
	Online Dating for Singles - Religion and Spirituality	1.728	1.830
	Fashion and Style - Reproductive Health	1.723	1.095
North America	Education and achievements - Reinsurance and capital markets	-0.148	-0.364
	Family dynamics and relationships - Nike shoes and fashion	0.109	0.691
	Reading and fiction - Cape Cod news	0.251	0.506
	Family relationships - Religious beliefs and figures	0.305	0.267
	Woodworking plans and projects - Music record and Artists	0.056	-0.258
Oceania	Weight loss and nutrition for women - Building and designing boats	0.336	0.582
	Exercises for hormone development - Superheroes and their Universes	-0.05	-0.07
	Kids’ furniture and decor - Building and designing boats	0.612	0.524

As expected, the family–career pair shows the highest bias across all three general IAT topic pairs. There are smaller differences among the other math–arts and science–arts.We also note that some pairs, such as dating and relationships advice–religious and spiritual growth (P5) for Africa, hotel royalty–political leadership in India (P1) for Asia, obituaries and geneology–Christian theology (P4), education–music (P1), and fashion and style–Christian theology (P5) for Europe, and online dating–religion and spirituality (P1), fashion and style–reproductive health (P2) for North America have differences higher than those for family–career in the respective regions, indicating that the participants associated more biases on our uncovered bias dimensions than the existing one in WEAT.These findings support our hypothesis that gender bias dimensions vary across regions and also bring preliminary evidence that the region-aware bias dimensions we uncover are in line with the human perception of bias in those regions.We also find that all the regions have biases that conform to our topic pairs gender association except P3: education–reinsurance and capital markets in North America, where the associated bias is negative.These findings confirm that topic pairs indeed differ across regions and that these differences must be taken into consideration when identifying and evaluating biases.

5 WEAT-based Evaluation Using Region-aware Topic Pairs

To measure biases in different data domains and regions, we extract region-aware topics using the GeoWAC dataset which spans Common Crawl separated by regions, and create a WEAT-style evaluation setup using these topics.

Data. We consider two datasets: (i) Reddit data and (ii) UN General Debates Baturo etal. (2017). The Reddit data consists of data from subreddits corresponding to specific regions: r/asia, r/africa, r/europe, r/northamerica, and r/oceania. We use the official Reddit API to extract data, consisting of 500 top posts²²2The Official Reddit API has rate limits, therefore 500 top posts from each subreddit ensures an equal number of examples for each region. from each subreddit. The posts are pre-processed to remove URLs and signs, and each post contains at least 30 words. The UN General Debate Corpus (UNGDC) includes texts of General Debate statements from 1970 to 2016. These statements, similar to annual legislative state-of-the-union addresses, are delivered by leaders and senior officials to present their government’s perspective on global issues. We filter the countries for each region and extract 500 data points per region, maintaining equal representation across region.³³3Oceania has limited available countries in UNGDC, hence the adherence to 500 data points for each region.

Method. WEAT tests consist of keywords corresponding to each attribute and topic word sets like family-career and male-female terms. To create a similar setup, we utilize KeyBERT Grootendorst (2020) to gather top topic representations corresponding to each topic extracted from GeoWAC. For male/female terms, we use the same representative words from WEAT. To further make it specific to a particular region, we employ GPT-4 OpenAI etal. (2024) to generate common male/female names used in the regions and add them to the list. We provide the list of words in Table12 of Appendix F. We use fastText Bojanowski etal. (2017)⁴⁴4We choose fastText because it allows to extract embeddings of words that are not present in the target text (as our topics are derived from GeoWAC). embedding algorithm to generate embeddings of the lists and compute the distances between the topic words and male/female terms (like WEAT).

6 Alignment of Region-Aware Bias Dimensions with LLM outputs

To understand if LLMs generate similar biases as our region-aware bias topic pairs, we devise a persona generation task by LLMs. We prompt the LLM to output personas interested in different ‘topics’ from the topic pairs that we extract. Fig4 shows an example of the prompt given to an LLM to generate personas. We experiment with different LLMs: GPT-3.5 Brown etal. (2020), GPT-4, Mistral-7b-Instruct Jiang etal. (2023), Claude-3 Sonnet,⁶⁶6https://claude.ai/ and Gemini-Pro Team etal. (2024). Many studies use LLM-generated personas for multi-agent interactions in different settings in societies Park etal. (2023); Zhou etal. (2024). But, if an LLM generates biased personas, for example, a female persona takes care of children, and a male persona is strong and takes care of emergencies, this would lead to further biases in consequent tasks. Therefore, we employ persona generation to check for the presence of any biases in the personas created by LLMs. To measure biases, we find the number of matched LLM output persona genders to the genders of our topic pairs.We average our results over seven runs.

Results.We plot the results of persona gender mismatched by LLMs in Fig5. The y-axis shows % mismatch between the LLM generated persona gender and the gender of the topic in our topic pair. For example, a mismatch is when LLM outputs a persona with ‘female’ for Politics in Asia, which is a ‘male’ topic according to our findings. Regions with high representation: North America, Europe and Asia have fewer mismatches, with North America having the lowest mismatch. Conversely, less represented regions like Africa and Oceania show higher mismatch rates. Among models, Mistral-7b (7B) has the highest mismatch rate while Gemini-Pro (50T) has the least, which may stem from varying model sizes.Overall, all the models exhibit similar mismatch trends for both highly and less represented regions. Fewer mismatches in highly-represented regions show the importance of evaluation using region-specific topic pairs. Higher mismatches in underrepresented regions like Africa and Oceania suggest LLMs don’t mimic these areas’ biases, which can be beneficial. However, due to growing research on LLMs’ cultural alignment, a more precise, region-specific bias evaluation metric becomes essential.

7 Related Work

IAT is one of the earliest and well-known method for measuring implicit social biases in humans Greenwald etal. (1998). Inspired by the IAT, WEAT uses word embeddings to measure biases in text Caliskan etal. (2017). Another extension of WEAT is the Sentence Embedding Association Test (SEAT), which measures biases at the sentence level May etal. (2019). Additionally, various bias detection measures in NLP focus on post-training model predictions, such as gender swapping Stanovsky etal. (2019). Moreover, there are specific gender bias evaluation test sets in tasks like coreference resolution Rudinger etal. (2018); Zhao etal. (2018); Webster etal. (2018) and sentiment analysis Kiritchenko and Mohammad (2018b).

Several studies have emphasized the significance of considering cultural awareness in the study of social phenomena. The demographics of individuals can shape their worldviews and thoughts Garimella etal. (2016), potentially influencing their language preferences and biases in daily life. Notably, some studies have observed a bias towards Western nations in current LLMs Dwivedi etal. (2023). Recent research has focused on cross-cultural aspects of LLMs, including aligning them with human values from different cultures Glaese etal. (2022); Sun etal. (2023) and exploring them as personas representing diverse cultures Gupta etal. (2024). To the best of our knowledge, no previous work has proposed a data-dependent approach to extract region-aware bias topics. Given the known biases in LLMs, a region-specific metric could greatly lead to an accurate evaluation of biases. This research holds significant importance in addressing cross-cultural biases effectively.

8 Conclusion

In this paper, we proposed a bottom-up approach using data to identify region-aware topic pairs that capture gender biases across different regions. Our human evaluation results demonstrated the validity of our proposed region-aware dimensions.

We employed a region-aware WEAT-based evaluation setup to assess biases in two additional datasets: Reddit and the UN General Debate Corpus. The presence of region-specific biases in these datasets underscores the importance of a region-aware bias evaluation metric. Additionally, when examining LLM outputs against the gender associations in our region-aware bias topic pairs, we found that biases align closely for three highly represented regions: North America, Europe, and Asia. This emphasizes the value of region-aware topic pairs in bias evaluation of LLMs. Conversely, biases do not align well for Africa and Oceania, indicating that LLMs do not adopt these regions’ specific biases–a potential benefit. Yet, it also highlights the ’cultural alignment’ issue in LLMs. More research on the cultural alignment of LLMs underlines the need to consider region-specific bias topic pairs for all regions in future studies.

Future work includes incorporating testing different model/dataset combinations and topic-pair dependency on data. We aim to study biases in different languages and explore region-aware bias mitigation techniques.

9 Limitations

We utilized the GeoWAC corpus as our sole data source for extracting topic pairs from various regions. However, we acknowledge the importance of incorporating additional datasets in our future work. Additionally, our WEAT-based evaluation was conducted on relatively smaller datasets. So, we intend to conduct further analysis on a larger dataset to ensure a comprehensive evaluation based on WEAT.

Our study did not account for different languages due to the diverse linguistic landscape of the regions (continents) included in our study. However, the significance of conducting a more detailed analysis to examine variations among different countries would be interesting.

Unfortunately, we encountered difficulties in finding participants from Oceania for human validation. Moving forward, we plan to include insights and findings from Oceania and incorporate a larger population to ensure a more comprehensive human validation.

10 Ethical Considerations

When developing our region-aware topic pairs, it is essential to consider the ethical implications. Since we utilize a much broader aspect of culture, i.e. continents to distinguish among cultures, the region-aware topic pairs we extract may not translate to cultures of communities that are not well-represented in models. Hence, it is important that we utilize topic pairs carefully.

It has been found that AI models often tend to output responses that are Western, educated, industrialized, rich, and democratic Henrich etal. (2010). In our experiments, we see LLMs also generate biases having the highest alignment with the West. Therefore, LLM experiments also need to be utilized carefully.

Our Reddit data for the region-aware evaluation metric may contain offensive content. However, we have anonymized the data (removed the usernames).

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Region	Country	#Examples
	Nigeria	3,153,761
Africa	Mali	660,916
	Gabon	645,769
	India	12,327,494
Asia	Singapore	6,130,047
	Philippines	3,166,971
	Ireland	8,689,752
Europe	United Kingdom	7,044,434
	Spain	465,780
	Canada	7.965,736
North America	United States	8,521,094
	Bermuda	244,500
	New Zealand	94,476
Oceania	Palau	486,437
	Vanuatu	165,355

Appendix A GeoWAC dataset details

Table5 contain the total number of examples per country in a region. We consider the top three countries with the highest number of examples per region.

Appendix B F-M Dataset statistics

Table6 displays the total number of examples from female and male groups per region for the region-specific F-M dataset.

Region	Total	#Female	#Male
Africa	57895	20153	37742
Asia	56877	21400	35477
Europe	59121	21049	38072
North America	70665	27627	43038
Oceania	62101	25951	36150

Target words - Attribute words	Region	Region-specific p-value	Region-specific WEAT score	Original WEAT score, p-value
	Africa	0.016	1.798	1.81, 0.001
	Asia	0.007	1.508
Male names vs Female names	North America	0.04	1.885
- career vs family	Europe	$6\cdot 10^{-4}$	1.610
	Oceania	0.03	1.727
	Africa	0.003	1.429	1.06, 0.018
	Asia	0.045	1.187
Math vs Arts	North America	0.007	0.703
- Male vs Female terms	Europe	0.005	0.334
	Oceania	0.03	1.158
	Africa	0.048	1.247	1.24, 0.01
	Asia	0.004	0.330
Science vs Arts	North America	$1\cdot 10^{-5}$	0.036
- Male vs Female terms	Europe	$1\cdot 10^{-7}$	-0.655
	Oceania	$2\cdot 10^{-4}$	0.725
	Africa	$3\cdot 10^{-5}$	0.855	1.21, 0.01
Young people names	Asia	$4\cdot 10^{-4}$	0.917
vs old people names	North America	0.032	1.325
- pleasant vs unpleasant	Europe	0.009	0.917
	Oceania	0.014	0.947
	Africa	$1\cdot 10^{-5}$	0.008	1.28, 0.001
European American names	Asia	$1\cdot 10^{-6}$	-0.453
vs African American names	North America	0.009	1.29
- pleasant vs unpleasant	Europe	0.001	0.617
	Oceania	$1\cdot 10^{-4}$	0.492
	Africa	0.03	1.443	1.53, $<10^{-7}$
	Asia	0.009	1.001
Instruments vs Weapons	North America	0.01	1.202
- pleasant vs unpleasant	Europe	0.02	1.21
	Oceania	0.001	0.951
	Africa	0.002	0.312	1.5, $<10^{-7}$
	Asia	0.009	0.869
Flowers vs Insects	North America	0.003	0.382
- pleasant vs unpleasant	Europe	0.001	0.332
	Oceania	0.009	0.660
	Africa	0.008	0.835	1.38, 0.01
	Asia	0.02	1.201
Mental disease vs Physical disease	North America	0.008	0.692
- temporary vs permanent	Europe	0.04	1.382
	Oceania	0.009	1.620

Appendix C Cultural differences in biases using WEAT

Table7 shows the WEAT scores for all WEAT dimensions defined in Caliskan etal. (2017). We find that scores and p-values differ across regions for different dimensions. High bias dimensions differ across regions, hence it is important to consider region-specific topic pairs.

Appendix D Region-wise topic lists in GeoWAC

Table8 displays a comprehensive list of topics for female and male groups across all regions.

Region	Female	Male
Africa	Credit cards and finances, Royalty and Media, Trading strategies and market analysis, Dating and relationships guides, Parenting and family relationships, Fashionable Ankara Styles, women’s lives and successes, online dating	Fashion and Lifestyle, Male enhancement and sexual health, Nollywood actresses and movies, Nigerian politics and government, Essay writing and research, Medical care for children and adults, Journalism and Media Conference, Music industry news and releases, Football league standing and player performances, Academic success and secondary school education, Religious inspiration and spiritual growth, Economic diversification and Socio-economic development
Asia	Hobbies and Interests, Healthy eating habits for children, Social media platforms, Royal wedding plans, Online Dating and Chatting, Adult Services, Gift ideas for Valentine’s Day	DC comic characters, Mobile Application, Philippine Politics and Government, Sports and Soccer, Career, Bike enthusiasts, Artists and their work, Youth Soccer Teams, Career in film industry, Political leadership in India, Bollywood actors and films, Religious devotion and spirituality, Phone accessories
Europe	Pets and animal care, Fashion and Style, Education, Obituaries and Genealogy, Luxury sailing, Traveling, Energy and climate change, Family and relationships, Pension and costs, Tech and business operations, Dating, Comfortable hotels, Government transportation policies	Political developments in Northern Ireland, Christian Theology and Practice, Crime and murder investigation, EU Referendum and Ministerial Positions, Criminal Justice System, Israeli politics and International relations, Cancer and medications, UK Government Taxation policies, Art Exhibitions, Political decision and impact on society, Music Gendres and artists, Medical specialties and university training, Political discourse and parliamentary debates
North America	Pets, Cooking: culinary delights and chef recipes, Fashion and style, Family dynamics and relationships, Reading and fiction, Scheduling and dates, Life and legacy of Adolf Hitler, Gender roles and inequality, Education and achievements, Online dating for singles, Luxury handbags, Footwear and Apparel brands, Essay writing and literature	Civil War and history, Middle East conflict and political tensions, Movies and filmmaking, Political leadership and party dynamics in Bermuda, Rock Music and songwriting, Wartime aviation adventures, Religion and Spirituality, Reproductive health, Reinsurance and Capital markets, Nike shoes and fashion, Cape Cod news, NHL players
Oceania	Cooking and culinary delights, Romance, Weight loss and nutrition for women, Water travel experience, Woodworking plans and projects, Time management and productivity, Inspiring stories and books for alleges, Sexual violence and abuse, Car insurance, Exercises for hormone development, kid’s furniture and decor	Harry Potter adventures, Art and Photography, Superheroes and their Universes, Music recording and Artists, Football in Vanuatu, Pet care and veterinary services, Building and designing boats, Religious beliefs and figures, Fashion, Classic movie stars, Men’s hairstyle and fashion, Male sexual health and supplements

Region	Female topics	Male topics
Africa	Credit card-based financial services	Fashion - footwear and celebrities
	Royalty and femininity	Male enhancement and sexual health
	Financial trading	Nollywood
	Dating guides	Nigerian politics
	Motherhood and parenting	Academic writing
Asia	Hobbies	Superhero comic books
	Food and nutrition	Mobile applications
	Social media platforms and content creation	Philippines politics and people
	Royal weddings	Sports
	Online social interaction and dating	Career
Europe	Pets	Irish politics
	Fashion	Christianity
	Education	Law enforcement and crime
	Deaths and funerals	EU and Brexit
	Luxury yachting and sailing	Criminal justice system
North America	Pets	Civil War Military
	Cooking and Food	Middle Eastern politics and conflicts
	Fashion	Movies and direction
	Family and relationships	Bermuda politics
	Reading novels	Rock music
Oceania	Food and eating habits	Harry Potter
	Romance and emotions	Artistic expressions
	Weight loss and nutrition	Superheroes of Marvel and DC
	Boat and sailing experience	Albums, songs and artists
	Woodworking and carpentry	Vanuatu Football

Towards Region-aware Bias Evaluation Metrics (9)

Appendix E Unigram/Bigram Analysis

Topic	Region	Unigrams	Bigrams
	Africa (male)	march, outlet, air, max, tods, man, said, pas, cher, people	air max, pas cher, princess j, roshe run, nike air, tods outlet, j march, roger vivier, posts email, notify new
Fashion and lifestyle	Europe (female)	one, women, fashion, like, new, look, make, hair, girl, dress	oakley sunglasses, louis vuitton, red carpet, new york, fashion model, engagement rings, per cent, year old, christian louboutin, diamond ring
	North America (female)	one, love, like, little, new, made, time, get, make, women	s cooper, cooper main, t shirt, new york, little girl, men women, look good, main store, years ago, check out
	Africa (female)	music, song, album, new, video, single, one, singer, also, songs	music industry, hip hop, record label, single titled, new single, chris brown, tiwa savage, ice prince, kanye west, niegrian music
Music	Europe (male)	man, single, stage, years, world, many, metal, guitar, solo, irish	year shelfmark, black metal, time exercise, musical content, dundee repertory, singer songwriter, edinburgh year, zumba days, male vocalists, millions men
	Oceania (male)	music, album, new, songs, band, first, time, jazz, released, rock	new york, elizabth ii, debut album, years later, big band, rock roll, first time, studio album, los angeles, solo artist

Table10 shows the unigrams and bigrams of common topics with different gender associations. We find that ‘fashion’ is highly associated with shoes when it is a male topic in Africa, whereas in Europe and North America, it is mostly associated with accessories like sunglasses, rings, etc. This shows the typical association of women with jewelry and men with shoes Russell (2010); Nichols (2011). In the case of ‘Music’, we see that unigrams and bigrams pertaining to Africa contain words related to hip-hop music and artists. For Europe, we find location references and metal music. And finally, Oceania shows references of jazz and rock. We do not find any obvious gender associations in the analysis of the music topic.Table11 provides a unigram/bigram analysis of topics that are commonly associated with a specific gender across regions. For parenting and family relationships, Africa has mentions of children, while Asia and Oceania contain mentions of family events, etc. In North America, we mostly find text about maintaining health in families.For religion and spirituality, the unigrams/bigrams are mostly about Jesus and Christianity across regions. For politics, we find mentions of specific regions, as expected. Education topic is more about being successful in Europe, where it is about degrees in North America. Finally, ‘social media’ trends are mostly similar. Overall for topics with same gender associations across regions, do not have stark differences.

Topic	Region	Unigrams	Bigrams
	Africa (female)	child, registration, form, information, sent, women, foster, best, catholic, women	registration form, form information, child assigned, surgery doctors, new catholic, catholic women, contemporary challenge, best everything, foster short, doctors clinic
Parenting and family relationships	Asia (female)	year, old, weekly, fortnightly, clicking, create, alert, state, 1, terms	year old, weekly fortnightly, create alert, stated agree, conditions acknowledge, finals appearances, together playing, dial guarded, came work, outlet jackets
	North America (female)	women, healthday, loss, three, worked, closely, together, she, elegant, dignified	three women, women worked, closely together, elegant dignified, very pleasant, soft spoken, women men, healthday reporter, tuesday march, participate more
	Oceania (female)	laurel, school, moved, one, day, royal, wedding, house, sister, hopefully	moved one, royal wedding, laurel school, 1 california, weeks dad, high school, one hopefully, nobody knew, sister means, fu school
	Africa (male)	god, man, church, one, life, people, jesus, us, lord, christ,	short description, jesus christ, man god, holy spirit, god said, thank god, bible says, catholic church, today god, every man
	Asia (male)	life, jesus, us, church, one, man ,lord, said, father, christ	holu spirit, jesus christ, pope francis, brothers sisters, son god, men women, holy father, opus dei, eternal life, paul ii
Religion and Spirituality	Europe (male)	god, one, jesus, church, life, people, father, man , said, christ	jesus christ, son man, catholic church, holy spirit, men women, said him, holy father, john paul, jesus said, word god
	North America (male)	god, jesus, one, man, us, life, would, christ, lord, people	recognizable cheering, section league, jesus christ, exact synonyms, past years, god said, years before, thanks mostly, mostly steph, father dell
	Oceania (male)	also, said, best, love, new, come, good, like, men, made	god said, jesus christ, holy spirit, lord krishna, temple god, father devil, eternal life, son god, son man, god father
	Asia (male)	said, one, India, time, people, minister, government, years, state, police, court	indian congress, government plans, modi ministry, human rights, foreign politics, armed forces, international warfare, foreign ministry, middle east, united nations
Politics	Europe (male)	government, said, minister, people, international, country, one, foreign, president, state	make statement, prime minister, human rights, armed forces, secretary state, middle east, united nations, hon friend, foreign secretary, united states
	Europe (female)	school, primary, teacher, founder, CEO, judgment, group, named, ranking, prestigious	as founder, founder CEO, judgment group, named fortune, ranking prestigious, world scientist, scientist women, students comprehend, program support, support students
Education	North America (female)	bachelor, years, student, leader, degree, animal, veterinary, music, taught, communication	bachelors degree, animal veterinary, bachelor music, alison taught, privately years, students ranging, development programmes, including leader, art communication, recent years
	Africa (male)	onigbinder, aura, pictures, first, gained, popularity, match, beaut, designed, music	aura pictures, gained popularity, match beaut, designed wonder, attending music, music festival, schomburg library, Instagram account, sugar coating, schedule tomorrow
Social Media	Asia (male)	time, later, latest, tracks, speedy, Zulfiqar, nasty, children, tweeted, guys	gets later, latest tracks, speedy zulfiqar, children pti, pti tweeted, taking long, long time, hosted pageant, time vincent, love fleeting

Appendix F WEAT-based evaluation setup details

For male/female terms, we use the same representative words from WEAT: brother, father, uncle, grandfather, son, he, his, him, man, boy, male for male and sister, mother, aunt, grandmother, daughter, she, hers, her, woman, girl, female for female.We also utilize GPT-4 to output the ten most common male/female names specific to each region. We provide the lists of word belonging toeach topic in Table12.

Region	Topics: Word lists
	Nollywood Actress and Movies: nollywood, actress, actors, drama, celebrity, movie, acting, movies, producer, tv
	Parenting and family relationships: mother, mom, mothers, mum, moms, parent, her, child, momodu, parents
	Sports and Football: players, sports, fifa, team, player, football, mourinho, scored, league, champions
	Marriage and relationships: wives, marriage, husbands, marriages, married, wife, relationships, husband, marry, relationship
	Fashion and lifestyle: cher, nike, max, air, looked, face, love, tods, soldes, scarpe
AFRICA	Womens lives and successes: women, ladies, woman, female, girls, men, gender, ones, employees, male
	Social Media: instagram, facebook, social, twitter, tweet, snapchat, tweets, tweeted, hashtag, followers
	Music: song, songs, album, hits, music, released, rap, singer, tracks, rapper
	Religious and Spiritual Growth: god, almighty, bible, christ, faith, believers, christian, jesus, prayer, religion
	Dating and relationships advice: dating, women, relationships, ladies, sites, singles, online, single, escorts, websites
	Male terms: male, man, boy, brother, he, him, his, son, Kwame, Mandela, Moyo, Jelani, Tariq, Keita, Obi, Simba, Ayo, Kofi, Jabari, Tunde, Mekonnen, Anwar, Chukwuemeka
	Female terms: sister, mother, aunt, grandmother, daughter, she, hers, her, Aisha, Zahara, Nia, Sade, Amara, Chinelo, Layla, Ayana, Nala, Zuri, Imani, Lola, Kamaria, Nyala, Kaya
	Political Leardership in India: modi, political, said, bjp, told, says, leader, congress, minister, public
	Hotel Royalty: visited, places, stayed, hotels, adventure, pictures, favourite, guest, hiking, hemingway
	Sports and Soccer: sports, team, basketball, players, nba, league, championship, coach, rebounds, finals
	Healthy eating habits for children: food, foods, eating, meals, nutrition, cuisine, diet, dishes, cooking, eat
	Social Media platforms for video sharing: instagram, video, videos, twitter, tweet, facebook, gifs, vlog, youtube, followers
ASIA	Royal wedding plans: meghan, duch*ess, engagement, england, royal, royalty, prince, kate, london, married
	Religious devotion and spirituality: god, bible, holy, faith, prayer, believe, christian, blessed, christ, spiritual
	Royal wedding plans: meghan, duch*ess, engagement, england, royal, royalty, prince, kate, london, married
	Bollywood actors and films: bollywood, bachchan, kapoor, actors, acting, kareena, actor, film, shahrukh, hindi
	Marriage: married, marriage, marriages, couple, couples, wife, marry, wedding, husband, divorced
	Male terms: male, man, boy, brother, he, him, his, son, Hiroshi, Ravi, Kazuki, Jin, Satoshi, Rohan, Haruki, Dai, Akira, Yuan
	Female terms: sister, mother, aunt, grandmother, daughter, she, hers, her, Sakura, Mei, Aiko, Yuna, Lina, Ji-hye, Mika, Nami, Anika, Rina
	Music: music, songs, vocalists, album, albums, singing, vocals, singles, rock, song
	Education: school, schools, classroom, students, education, educational, pupils, boys, academy, college
	Political decisions and impact on society: government, public, minister, said, hon, people, first, the, column, committee
	Comfortable hotels: guests, staying, rooms, friendly, welcoming, stayed, hotel, beds, stay, comfortable
	UK Government Taxation Policies: corbyn, taxation, fiscal, tax, taxes, exchequer, labour, governments, government, deficit
EUROPE	Luxury Sailing: yachts, yacht, boat, sailing, sails, cruising, sail, berths, cruiser, cabin
	Christian Theology and Practice: god, bible, christ, jesus, faith, christian, religious, religion, holy, gave
	Obituaries and Genealogy: died, edward, relatives, anne, lived, elizabeth, funeral, irish, mrs, galway
	Christian Theology and Practice: god, bible, christ, jesus, faith, christian, religious, religion, holy, gave
	Fashion and style: fashion, shoes, style, clothes, clothing, shoe, wear, nike, dress, stylish
	Male terms: male, man, boy, brother, he, him, his, son, Lukas, Matteo, Sebastian, Alexander, Gabriel, Nikolai, Maximilian, Leonardo, Daniel, Adrian
	Female terms: sister, mother, aunt, grandmother, daughter, she, hers, her, Emma, Sophia, Olivia, Isabella, Ava, Mia, Charlotte, Amelia, Lily, Emily
	Religion and Spirituality: god, christ, jesus, bible, christian, holy, christians, scripture, faith, heaven
	Online Dating for Singles: dating, singles, hookup, single, relationships, dates, flirting, personals, date, mingle
	Reproductive Health: download, available, pdf, online, edition, manual, free, reprint, kindle, file
	Fashion and style: fashion, dresses, dress, wardrobe, clothes, clothing, style, outfit, vintage, wear
	Reinsurance and capital markets: reinsurance, reinsurers, insurers, insurance, securities, investors, investment, finance, trading, pension
NORTH	Education and achievements: school, schools, graduated, college, students, undergraduate, graduation, graduate, attended, education
AMERICA	Nike shoes and fashion: nike, shoes, sneakers, jordans, jeans, tops, black, boys, men, casual
	Family dynamics and relationships: family, families, children, kids, grandchildren, relatives, grandparents, parents, child, parent
	Cape Cod news: lifeguard, drowned, drowns, newstweet, hospitalized, snorkeling, cape, reported, reuterstweet, pulled
	Reading and fiction: books, book, reading, novels, series, enjoyed, novel, romance, katniss, readers
	Male terms: male, man, boy, brother, he, him, his, son, Liam, Noah, Ethan, Jacob, William, Michael, James, Alexander, Benjamin, Matthew
	Female terms: sister, mother, aunt, grandmother, daughter, she, hers, her, Emma, Olivia, Ava, Sophia, Isabella, Mia, Charlotte, Amelia, Harper, Evelyn
	Religious beliefs and figures: god, gods, bible, mankind, faith, christ, spiritual, christian, religion, jesus
	Family relationships: mum, mother, mom, mums, parent, family, parents, baby, dad, father
	Music record and Artists: music, album, albums, jazz, songs, hits, musicians, artists, recordings, blues
	Woordworking plans and projects: plans, furniture, woodwork, wood, woodcraft, woodworking, plywood, carpentry, cabinets, wooden
	Building and designing boats: boatbuilder, boatbuilding, boats, plans, boat, sauceboat, sailboat, build, catamaran, kits
	Weight loss and nutrition for women: diet, workout, exercise, foods, weight, food, eating, healthy, pounds, fat
OCEANIA	Superheroes and their Universes: superhero, superheroes, avengers, marvel, comics, superman, aquaman, heroes, comic, hero
	Exercises for hormone development: hormones, weightlifting, workouts, deadlifts, hormonal, exercises, lifting, testosterone, fitness, squats
	Building and designing boats: boatbuilder, boatbuilding, boats, plans, boat, sauceboat, sailboat, build, catamaran, kits
	Kids furniture and decor: furniture, chairs, sofas, ikea, sofa, cushions, sectional, upholstered, couch, childrens
	Male terms: male, man, boy, brother, he, him, his, son, Manaia, Tane, Kai, Ariki, Mika, Koa, Rangi, Kane, Tama, Hemi
	Female terms: sister, mother, aunt, grandmother, daughter, she, hers, her, Aroha, Moana, Tui, Lani, Kahurangi, Ariana, Malie, Marama, Ava, Kaia

Appendix G Paired-list for F-M datasets

Here is the list of the 52 pairs used to create the F-M datasets per region:
[monastery, convent], [spokesman, spokeswoman], [Catholic priest, nun], [Dad, Mom], [Men, Women], [councilman, councilwoman], [grandpa, grandma], [grandsons, granddaughters], [prostate cancer, ovarian cancer], [testosterone, estrogen], [uncle, aunt], [wives, husbands], [Father, Mother], [Grandpa, Grandma], [He, She], [boy, girl], [boys, girls], [brother, sister], [brothers, sisters], [businessman, businesswoman], [chairman, chairwoman], [colt, filly], [congressman, congresswoman], [dad, mom], [dads, moms], [dudes, gals], [ex girlfriend, ex boyfriend], [father, mother], [fatherhood, motherhood], [fathers, mothers], [fella, granny], [fraternity, sorority], [gelding, mare], [gentleman, lady], [gentlemen, ladies], [grandfather, grandmother], [grandson, granddaughter], [he, she], [himself, herself], [his, her], [king, queen], [kings, queens], [male, female], [males, females], [man, woman], [men, women], [nephew, niece], [prince, princess], [schoolboy, schoolgirl], [son, daughter], [sons, daughters], [twin brother, twin sister].
Each pair in the above is denoted as a [male, female] pair.

Appendix H Llama 2 prompt for topic modeling

The prompt scheme for Llama2 consists of three prompts: (1) System Prompt: a general prompt that describes information given to all conversations, (2) Example Prompt: an example that demonstrates the output we are looking for, and (3) Main Prompt: describes the structure of the main question, that is with a given set of documents and keywords, we ask the model to create a short label for the topic. Fig6 displays the three prompts as used in the code.

Appendix I Topic Cluster Labels using other LLMs

We use Llama2 to fine-tune our topics to label them for better coherence in our paper. However, we also experiment with GPT-4 and arrive at similar topics in Table9. (see Table2 for comparison with Llama2 topic labels).

Appendix J Topic Word Clusters Example - Africa

Here, we provide an example of how topics look in our data. In Fig7, we provide word clusters of topics from Africa. The word clusters contain the top 10 words from each topic in Africa. We find that topic labels by Llama2 are coherent in terms of top topic words.

Towards Region-aware Bias Evaluation Metrics (10)

Appendix K Region specific BERTs to identify top words in F/M direction

To motivate our case to investigate differences in biases across regions, we use BERT to compute the top words corresponding to the she-he axis in the embedding space. BERT is a pre-trained transformer-based language model that consists of a set of encoders. As a motivation experiment to identify differences in the contextual embedding space for different regions, we fine-tune BERT with the masked language modeling task (no labels) for each region separately. For a given word, we compute its embeddings by averaging out all sentence embeddings where it occurs across the dataset.Similarly, we compute embeddings for all words in the dataset. The tokenized input goes through the BERT model and we take the hidden states at the end of the last encoder layer (in our case, BERT-base, i.e. 12 encoder layers) as sentence embeddings. We identify the top words with the highest projection across the she-he axis in the region-specific datasets. If we find differences in the top words across regions, it is possible that dominating bias topics vary by region as well. Fig8 shows the top words closest to ‘she’ and ‘he’ contextual embeddings in our data for each region. We find that top words differ quite a bit across different regions.We find many differences in the top F (close to $she$ ) and M (close to $he$ ) words across regions. Some top F words are soprano, archaeological (Africa); graduate, secretary (Asia); innovative, graphics (Europe); poets, sentiments (NA); and arts, sleep (Oceania). Some top M words are history, leading (Africa); astronomer, commissioners (Asia); honorary, songwriters (Europe); owner, hospital (NA); and wrestlemania, orbits (Oceania). Gender-neutral words such as poets, secretaries, astronomers, commissioners, songwriters, owners, and so on are closer to either the she or he axes. Although comparable to the findings of Bolukbasi etal. (2016), the variances among regions inspire us to look deeper into the data to arrive at culture-specific bias themes.

Appendix L Implementations details

For training our Bertopic model, we use Google Colab’s Tesla T4 GPU, and it takes 15 min to run topic modeling for a region-specific F-M dataset. Region-specific BERTs are run on NVIDIA RTX2080 GPUs. Each BERT training experiment takes 1 GPU hour. For our LLM experiment, we used NVIDIA-A40 for Mistral-7b-Instruct and Llama-3-8b for an hour. We do not use any GPUs for GPT-4, Claude-3-Sonnet and Gemini-Pro.

L.1 Bertopic

We use Bertopic’s default models: SBERT Reimers and Gurevych (2019) to contextually embed the dataset, UMAP McInnes etal. (2018) to perform dimensionality reduction, HDBSCAN Malzer and Baum (2020) for clustering to perform topic modeling. We choose the embedding model BAAI/bge-small-en from Huggingface Wolf etal. (2019). We set top_n_words to 10 and verbose as True and set the min_topic_size to 100 for the Bertopic model. Finally, we use Bertopic’s official library to implement the model.

L.2 Llama2

We use Llama2 to finetune the topics to give shorter labels for each topic. We set the temperature to $0.1$ , max_new_tokens to $500$ and repetition_penalty to $1.1$ . We utilize Bertopic’s built-in representation models to use Llama2 in our topic model.

L.3 LLM experiment

For GPT-4, and Mistral-7b-Instruct and Llama-3-8b, we utilize the Microsoft Azure API⁷⁷7https://learn.microsoft.com/en-us/rest/api/azure/, huggingface⁸⁸8https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1, and huggingface⁹⁹9https://huggingface.co/meta-llama/Meta-Llama-3-8B for inference respectively. We use a temperature $0.8$ for all models. For Gemini-Pro and Claude-3-Sonnet, we use the available chat interface.

L.4 Region-specific BERT

We use the uncased version BERT Devlin etal. (2019) for our region-specific BERT model trained for the MLM objective. We use a batch size of 8, a learning rate of $1\cdot 10^{-4}$ , and an AdamW optimizer to train our BERT models for 3 epochs.

Towards Region-aware Bias Evaluation Metrics (11)

Towards Region-aware Bias Evaluation Metrics (12)

Appendix M Human Validation

Students and staff from a college campus were recruited as annotators in the study. Screenshots of the form are displayed in Fig9. We have 6 annotators per region (3 male and 3 female).

Towards Region-aware Bias Evaluation Metrics (16)

Towards Region-aware Bias Evaluation Metrics (17)

Appendix N Reproducibility

We open-source our codes, which are uploaded to the submission system. We include commands with hyperparameters in our codes. This would help future work to reproduce our results.