Ableism, Ageism, Gender, and Nationality bias in N orwegian and Multilingual Language Models (2025)

Abstract: We investigate biases related to ageism, ableism, nationality, and gender in four Norwegian and two multilingual language models. Our methodology involves using a set of templates. constructed around stimuli and attributes relevant to these categories. We use statistical and predictive evaluation methods, including Kendall’s Tau correlation and dependent variable prediction rates, to assess model behaviour and output bias. Our findings indicate that models frequently associate older individuals, people with disabilities, and poorer countries with negative attributes, potentially reinforcing harmful stereotypes. However, most tested models appear to handle gender-related biases more effectively. Our findings indicate a correlation between the sentiment of the input and that of the output.

## introduction bias in large language models (llms) can emerge at various stages of a model's lifecycle, from data collection to deployment. during the data collection phase, biased data may inadvertently be included in the training data, particularly if it reflects historical stereotypes, social biases, or the underrepresentation of certain groups. in the training phase, llms learn patterns from this data, potentially amplifying the existing biases. these biases are subsequently encoded in the model's parameters, influencing the generation of responses. the term bias has been defined in various ways the context of llms. in the context of nlp, this includes representational harms (misrepresentation, stereotyping, disparate system performance, derogatory language, and exclusionary norms) and allocational harms (allocating or withholding opportunities or resources from specific groups or individuals.) @xcite . if not properly addressed, these biases may reinforce social divisions by perpetuating stereotypes. identifying and mitigating such biases is therefore crucial for developing fair and responsible ai systems. very little work has been done on social biases in llms for the norwegian language, and most of the work has focused on gender bias @xcite @xcite @xcite . we therefore focus on the norwegian language, and explore social biases beyond gender. here we investigate two research questions: 1) to what extent do llms exhibit ageism, ableism, gender, and nationality bias in their generated outputs in norwegian? and 2) are the levels of ageism, ableism, gender, and nationality bias in norwegian llms comparable to those in multilingual llms? 2 background @xcite identified three primary sources of bias in llms: training data bias, embedding bias, and label bias. training data bias arises from the quality and characteristics of the data, which can reflect historical inequalities, social stereotypes, and underrepresentation of certain groups. this bias can be exacerbated by inappropriate content such as hate speech. embedding bias occurs when these biases are encoded into the model's vector representations, affecting semantic relationships and potentially leading to skewed outputs @xcite . label bias is introduced by human annotators during the labelling process, where subjective judgments can influence the model's learning and decision-making, resulting in unfair outcomes @xcite . these biases collectively impact the performance and fairness of llms, necessitating comprehensive strategies to mitigate their effects. quantifying bias in llms is a multifaceted endeavour, with researchers employing various methodologies to assess and measure it. three principal approaches have been identified: embeddingbased metrics, probability-based metrics, and generation-based metrics @xcite . embedding-based metrics, for instance, use vector representations to evaluate bias by measuring distances between words or sentences in the embedding space. the word embedding association test (weat), proposed by @xcite , and its extension, the sentence encoder association test (seat) by @xcite , exemplify methods that reveal biases in static and sentence embeddings, respectively. these intrinsic metrics focus on a model's internal representations, yet some researchers argue that biases detected in the embedding space may not necessarily translate to downstream tasks, necessitating complementary evaluations @xcite @xcite . there is a predominant focus on gender bias (48%) in the literature, followed by nationality (7%), ableism (5%), and ageism bias (4%) @xcite . ageism manifests through negative assumptions about older adults' abilities and relevance @xcite . ableism, reflects discrimination against individuals with disabilities, where models have been shown to underrepresent disabilities and associate negative attributes with disability-related terms @xcite . nationality bias refers to the tendency to associate certain nationalities with specific attributes (either positive or negative), often reflecting stereotypes @xcite @xcite . we identify bias in system behaviours where models exhibit preferential or discriminatory ten-dencies based on attributes such as gender, age, nationality, or disability. this is especially true when models consistently produce responses that reinforce stereotypes, fail to select appropriate alternatives, or generate outputs that are influenced by irrelevant contextual factors in the input. these biases can be harmful in various ways. biased behaviours reinforce societal stereotypes, perpetuating harmful prejudices and contributing to the marginalisation of certain groups. for instance when models predominantly associate negative attributes with specific genders or nationalities. also, bias leads to inaccurate and unfair representations, and can result in exclusion and discrimination, especially when models fail to appropriately handle attributes related to disability. these harmful effects primarily impact marginalised and underrepresented groups, including women, older individuals, people with disabilities, and minority nationalities, exacerbating existing societal inequalities. ## methodology we use a fill-in-the-blank approach to investigate biases in llms, a method widely used to measure bias in various domains @xcite . our approach aims to determine whether llms make general associations between stereotyped categories and unrelated positive, negative, or neutral attributes, rather than inferring specific stereotypes. when we use the terms positive and negative to categorize different social groups, this terminology is solely for analytical purposes. no age groups, individuals with or without disabilities, or countries are inherently better or more valuable than others. the social groups in the negative category are simply the ones we believe to be more frequently exposed to bias and stereotypical prejudice, such as older people, poorer countries, and people with disabilities. we follow the work of @xcite and examine bias in two directions which involves inferring an attribute given a social group and vice versa. we believe that testing how a model makes associations in both directions provides a broader basis for comparison and evaluation. following @xcite , we define the term stimulus to refer to the description used in the fill-in-the-blank sentences based on the bias category. our primary experiments are divided into two main directions @xcite involves providing a social group as the stimulus and inferring an associated attribute, while the asa direction involves providing an attribute and inferring the associated social group. figure 1 shows an example of the task in both directions. in the sai experiment, a stimulus is provided, and the model is tasked with inferring a related attribute. the model then selects from a set of three attributes: positive, negative, and neutral. while in the asa experiment, an attribute is provided, and the model is tasked with selecting a specific stimulus. here again, the model must choose between a set of three stimuli that are also classified as positive, negative, or neutral. ## data we use the ageism, gender, and nationality bias dataset of @xcite , we adapt it and translate it into norwegian. we use gpt-4 foot_1 to translate from english to norwegian, as our tests showed that it required minor adjustments for accuracy. translations were then manually verified and corrected if necessary. this included checking the grammatical gender. the dataset includes both singular and plural references, and gender-neutral terms like "they" or "people". we also extend this dataset by manually creating sentences about people with disabilities. this required designing template sentences, defining attributes for people with and without disabilities, and selecting appropriate stimulus adjectives. attributes were sourced from nrk (2024), a glossary of neutral and non-offensive functional diversity words compiled by the norwegian broadcasting corporation. for ageism, the positive group includes individuals aged 25-35, while the negative group includes those aged 60-70. for nationality bias, countries were divided into positive and negative groups based on gdp per capita, with the 15 richest and 15 poorest countries representing each group respectively. the distribution of instances of each bias in our dataset can be seen in ## experimental setup and methods pre-trained language models we use six different llms: four norwegian models and two multilingual models that support norwegian. these models were selected for their diverse training datasets, encompassing both norwegian and multilingual corpora, and their mix of architectures. we use the following models: • normistral-warm-instruct: a norwegian model from the nora llm family foot_2 , initialized from mistral-7b-v0.1 @xcite and instruction-tuned on open datasets. ## evaluation we use two metrics to assess the presence of biases in models: dependent variable prediction rates (dvpr) and kendall's tau (τ ) correlation coefficient. both metrics, adopted from kamruzzaman et al. (2024), were tested on all models. dvpr offer an intuitive measure of how fre-quently a model assigns favourable characteristics to different social groups, facilitating easy interpretation and comparison across models, thereby identifying potential disparities in attribute association. conversely, kendall's τ quantifies the ordinal association between two numerical sequences by comparing the relative ordering of all possible pairs, providing a robust statistical measure of agreement. dvpr we analyse how llms associate positive and negative attributes with different stimuli by measuring the conditional probability of selecting positive, negative, or neutral attributes in response to various stimuli (sai). our analysis also covers the reverse direction, where llms infer a stimulus in response to different attributes (asa). these probabilities, termed [stimulus]-to-[attribute] likelihoods, depend on the context sentence direction @xcite . for example, if the model assigns positive attributes (e.g., vennlig-friendly, motivert-motivated, kreativ-creative), to stimuli that follows this works' definition of negative foot_6 (e.g., 65 år gammel-65 year old, mann med autismeman with autism, sør-sudan-south sudan) is termed negative-to-positive likelihood (npl). similarly, we define positive-to-positive (ppl), positiveto-negative (pnl), negative-to-negative (nnl), positive-to-neutral (pnul), and negative-to-neutral (nnul) likelihoods, where p stands for positive, n for negative, and nu for neutral. a model is considered biased if the likelihood of selecting positive, negative, or neutral attributes (or stimuli) shifts with the polarity of the stimulus (or attribute) @xcite . in an unbiased model, ∆p l, ∆n l, and ∆n ul should be zero, where: ∆p @xmath0, ∆n @xmath1, and ∆n @xmath2. consistently negative ∆n l indicates systematic adjustment of negative predictions in response to stimulus polarity changes @xcite . prediction rates are expressed as changes when shifting from positive to negative independent variable values. if there is no dependency between stimuli and attributes, values should remain close to zero, with minor random variations. direction of the relationship between two ordinal or ranked variables. it assesses the correspondence between the rankings of two variables by comparing the number of concordant and discordant pairs in the dataset @xcite . the coefficient ranges from -1 (perfect negative correlation) to +1 (perfect positive correlation), with 0 indicating no correlation. concordant pairs occur when the relative order of both variables is the same, while discordant pairs occur when the order is reversed. this work uses the tau-b variation, which accounts for ties in the data @xcite . the pairs are created by combining the input sentence with the model's response, with binary inputs (positive or negative) and ternary responses (positive, negative, or neutral). following @xcite , our null hypothesis posits no correlation between the input and the models' responses, with a significance level of α < 0.05 used to reject the null hypothesis. the τ test results for normistral show that the null hypothesis is rejected in three out of four settings, indicating a statistically significant correla-tion between the input variable and the model's response in these cases. the model exhibits biases in ageism, ableism, and nationality in these three settings, although the correlation is weaker compared to gpt-4 and llama3. the one-shot asa setting is the only case where the null hypothesis is not rejected, suggesting no bias in that scenario. for norwai-llama2 we see that the null hypothesis is rejected in two out of four settings, indicating a significant correlation between the input variable and the model's response in these cases. however, in the zero-shot sai and one-shot sai settings, the τ values are very low with p-values exceeding 0.05, so we fail to reject the null hypothesis, suggesting no bias. in the zero-shot asa and one-shot asa settings, the null hypothesis is rejected, indicating a correlation and potential bias. in normistral demonstrates rather good prediction rates overall, with smaller deviations from zero compared to the two other models. highest levels of bias are related to ableism, in both the sai and asa directions. similarly, norwai-llama2 exhibits strong prediction rates across all settings, but shows the highest level of bias concerning ableism. in the sai direction for ableism, the ∆pl rates are negative, indicating that the model is less likely to select a positive alternative when the context sentence refers to someone with a disability. the dependent variable prediction rates for nb-bert in the sai and asa directions are in we also analysed the percentage of times the models select positive, negative, or neutral alternatives, for both zero-shot and one-shot settings. gpt-4 generally shows positive sentiment, except for ableism in the asa direction, where positive attributes are chosen only 10-11% of the time. llama3 has a strong tendency towards negative responses, especially for ageism and ableism in the asa direction, while nationality-related sentences are more positive. normistral is relatively balanced, with an increase in neutral responses from sai to asa. norwai-llama2's polarity varies by bias type, showing the most positive bias for ageism in the sai direction but the least in the asa direction. nb-bert mostly provides positive or neutral responses, with negative responses being less frequent, except for ageism in the asa direction. norbert3 shows more positive responses for ageism and nationality in the sai direction, but more negative responses across all bias types in the asa direction. more details about this can be seen in in addition to this, we looked separately at gender bias and explored the kendall's τ correlation between female gender and positive outputs both for zero-shot and one-shot, respectively as noted with the other types of biases, the two norwegian generative models seem to be less biased for genders as well, in all combinations of settings sai, asa, zero-shot, and one-shot. there is however a notable exception, in one-shot setting, asa direction, with the model norwai-llama2 with regards to male gender. this means that the model is 14.71 percentage points more likely to generate a positive response when the input is posi- overall, the results indicate a positive correlation between positive input and positive output, with models reflecting the positivity of the input. conversely, they also reveal a negative correlation between positive input and negative output, as models avoid negativity when the input is positive and tend to be more negative when the input is negative. a statistical overview of the distribution of these categories can be seen in ## conclusion and discussion when examining inherent bias, the norwegian autoregressive models, normistral and norwai-llama2, are the least biased, consistently achieving prediction rates close to zero and exhibiting minimal bias. among the models tested, llama3 was notably the most biased, displaying the highest prediction rates overall, particularly for nationality bias, and exhibiting the most negative polarity. this model tends to select positive alternatives when the input referenced wealthy countries and negative alternatives for poorer countries. for ageism, llama3 showed good prediction rates but a high proportion of negatively chosen alternatives, indicating a consistently negative view of individuals regardless of their age. our findings reveal that the lms more frequently associate older individuals, people with disabilities, and poorer countries with negative attributes, such as lower adaptability or effectiveness. models also associate negative attributes with all genders, if the stimulus is negative. from a purely descriptive standpoint, these associations might reflect realworld trends. however, this reasoning becomes problematic normatively speaking, as it risks reinforcing harmful stereotypes rather than accounting for individual and contextual variability. regardless of the type of bias, our results indicate that the tested models tend to align with the polarity of the input: they are more likely to generate positive content when given positive prompts, and more negative content when the prompts are negative. these patterns suggest that the models are not neutral and are influenced by the polarity of the input, even when such polarity should be irrelevant. this indicates the presence of bias in the models. biases in language models do not merely mirror reality; they also shape it. when integrated into downstream applications, these biases can influence perceptions, decision-making processes, and ultimately how individuals and groups are treated by institutions and systems.