Which stereotypical nationality are you

S4 Table. Multivariable associations between stereotype endorsement and target racial group among White adults who work or volunteer with children. S5 Table. Population weighted estimates of mean levels of stereotype endorsement towards adults, by racial group, among White adults who work or volunteer with children, by on and off panel.

S1 File. Minimal dataset. References 1. Health Aff Millwood. View Article Google Scholar 2. Ethnic differences in children's socioemotional difficulties: Findings from the Millennium Cohort Study. View Article Google Scholar 3. Social and emotional outcomes of Australian children from Indigenous and culturally and linguistically diverse backgrounds.

Racial Disparities in Child Aversity in the U. American Journal of Preventive Medicine. American Behavioral Scientist. View Article Google Scholar 6. Understanding associations among race, socioeconomic status, and health: Patterns and prospects. Health Psychology. Is racism a fundamental cause of inequalities in health? Annual Review of Sociology. View Article Google Scholar 8.

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J Youth Adolescence. Perceived discrimination among african american adolescents and allostatic load: A longitudinal analysis with buffering effects. Child Development. Perceived discrimination and markers of cardiovascular risk among low-income African American youth. American Journal of Human Biology. The relationship of internalized racism to body fat distribution and insulin resistance among African adolescent youth.

J NatlMed Assoc. Discrimination, racial identity, and cytokine levels among African-American adolescents. Journal of Adolescent Health. Benner AP, Graham S. Developmental Psychology. Neuroscience, molecular biology, and the childhood roots of health disparities: building a new framework for health promotion and disease prevention. Self reported expeirences of discriminatinon and health: Scientific Advances, Ongoing Controversies, and Emerging Issues.

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Ethical dilemmas in research with children and young people about their social environments. This results in the advantage that rare words can be handled much better Hapke et al.

In this work, we focused on pre-trained word embeddings as they are often used in practice NLP applications.

Such word models are publicly available on the internet and can be used for different projects, unless a domain-specific word model is required Hapke et al. Therefore, bias contained in such off-the-shelf word embeddings can easily propagate to numerous applications. Previous work in the field did also consider pre-trained word embeddings, e. The word embeddings used in our experiment had dimensions and were trained on CommonCrawl and Wikipedia.

Table 1 compares the dataset sizes. The first row contains the sizes of each officially downloadable compressed archive containing all word vectors for each language 1. The second row shows the number of all Wikipedia articles for each individual language, while the third row contains the share of the Common Crawl corpus for each language. All Wikimedia stats come from the official Wikimedia List of Wikipedias 2. The Common Crawl share was obtained from the CommonCrawl statistics tool 3. These values describe on how much data the word embeddings for each language have been trained on.

Table 1. Size comparison of the different fastText word embeddings used in this paper. In the new experiments investigating origin bias in Switzerland, the German word embeddings from fastText are used. We translated and where necessary adapted the original experiment to Italian and Swedish. Table 2 shows the complete listing of the selected words. In the following paragraphs, we describe the particular selection and translation of words for each language:.

Table 2. Unfortunately, no direct data for names of foreign origin was available. We, therefore, used data from a website that proposes potential baby names by different categories and selected the most clicked names of foreign origin from the website's statistics 5. The pleasant and unpleasant words were translated from English. We replaced the word wonderful with incredibile Englisch: incredible to better suit the Italian language, as proposed by the native speaker.

Due to the same reason, we replaced nasty with sgradevole English: unpleasant and awful with terrificante English: terrifying. However, translations always contain subjectivity. Therefore, we further validated the impact of our choice as follows: we repeated the experiment by replacing incredibile by other options: magnifico, fantastico , and meraviglioso , as well as the female forms magnifica, fantastica , and meravigliosa. Swedish - For the typical Swedish names for men and women, we considered the data made available through Statistics Sweden 6.

Since no list of popular names of different origins in Sweden was available, we compiled a list of the most popular names of Afghan, Indian, and Syrian origin, proportional to the percentage of the population. The pleasant and unpleasant words were translated to Swedish from the original experiments in English. Table 3 shows the complete listing of the selected words.

In the following paragraphs, we describe particularities regarding the selection and translation of words for each language:. Table 3. Italian - For Italian, two different experiments were conducted. Once, we considered the most often used names in Italy from the Italian National Statistics Institute, as in the previous experiment.

In the second experiment, we considered the most common names in the Italian-speaking region of Switzerland, provided by the Swiss Federal Statistics Office 7.

The career and family words were in both cases translated from English to Italian. As opposed to English, in Italian often male and female versions of professions exist e. However, the female version is not used consequently, often the generic male version of the word is used for both genders. To avoid this having an impact on our results, we picked words in the word set that do not express gender e.

To further validate the impact of such personal words, we repeated the experiment by replacing the word dirigente with the word successo engl: success. Swedish - For the typical Swedish names for men and women, we considered the data made available through Statistics Sweden 8. We translated the English words from the original experiments to Swedish. However, the word corporation was replaced with the Swedish translation of company , and leader was used instead of executive to better match the Swedish language.

We translated and where necessary adapted the WEAT7 experiment from the original paper which considered math and arts words, and male and female words. Table 4 shows the complete listing of the selected words. Table 4.

We translated and where necessary adapted the WEAT8 experiment from the original paper which is considering science and arts words, and male and female words.

Table 5 shows the complete listing of the selected words. In the following paragraphs, we describe any particularities regarding the selection and translation of words for each language:. Table 5.

We considered NASA, Einstein, and Shakespeare as internationally known words and therefore kept these words for all the languages. However, Swedish does not have generalized terms for most relatives, it is always declared what side they are from, thus uncle becomes morbror mother's brother or farbror father's brother. We used the father's side for the male terms and the mother's side for the female terms in our experiments. As shown in our previous work, the form of bias in word embeddings in different languages can vary Kurpicz-Briki, We, therefore, designed additional experiments to identify origin bias in German word embeddings, based on word groups, in particular, representing the situation in Switzerland.

Table 6 shows the exact wording of the experiments. The next paragraphs give detailed information about the experimental setup for each experiment. Table 6. The terms from the new experiments investigating origin bias in Switzerland on German word embeddings. We have previously shown that there is bias when considering Swiss names and names of foreign origin in general Kurpicz-Briki, , and therefore we this time considered three concrete groups of names with foreign origin.

According to the Swiss Federal Statistical Office 9 , the three most common Non-EU countries of origin that are still in the European continent, which excludes origins such as Africa, the Americas, etc. We, therefore, used common names in proportional combination of these three countries at , using local sources 10 , 11 , We selected originally Swiss German names by using the eight most common names of the German part of Switzerland for women and men based on data from the Swiss Federal Statistical Office WEAT6-origin - Based on the WEAT6 experiment from the original paper, we designed a new experiment to investigate a potential origin bias regarding business for both men and women.

We, therefore, considered the Swiss names and names of foreign origin as described in the previous paragraph, conducting the experiment for women WEAT6-origin-f and men WEAT6-origin-m separately. We considered positive and negative words regarding business and inclusion in the system.

As positive words, the business terms from the original WEAT6 experiment were used. As negative words, a list of words including Sozialhilfe, Abbruch , or unqualifiziert was compiled English: welfare, dropping out, and unqualified. This experiment aims to investigate whether there is a bias toward people of different origin with regard to inclusion in society and labor market. All the selected words are gender-neutral, so that the same list of words was used in both experiments.

The word sets used to detect bias in word embeddings are based on the original experiments from the IAT from psychology Caliskan et al. We have previously shown that additional word sets can be identified by translating the word sets to other languages which does not always work or using real-world data, such as study choices or stereotypes from the eighteenth century Kurpicz-Briki, However, the identification of these word sets has been a manual process.

In this paper, we present BiasWords, a method to automatically detect new potentially biased word sets by exploring the vector space. As a proof of concept, we are using German word embeddings to demonstrate our approach. However, the same procedure could be applied to word embeddings of any language. We select the well-known word sets from literature that show bias and explore the vector space around each word in these word sets. Given the fact that word vectors being closer together have similar meaning, we expect that words being close to biased words might themselves also be biased.

In this proof of concept, we considered the experiments from our previous work, i. For each word in each of the word sets, the 20 nearest neighbors are identified and a selection procedure is applied: the part-of-speech tag must be compatible, and the words must differ by at least three letters. We require the difference of at least three letters to avoid other forms of the same word to be considered, e.

In particular, for the generated categories containing male e. Due to the fact that family matters are close in vector space, there is an overlap and words such as Ehemann engl. Neutral words that appeared in such categories e.

Words containing spelling mistakes were also kept. After this step, for each category of each of the existing word sets, there is a list of other words that are candidates for the new word set. We, then, chose random words from each list, the same number as the size of the original word sets.

We validated the newly created word sets using the WEAT method previously explained. In this section, the results of the previously described experiments are reported. In this work, we consider a statistically significant bias if the p -value is below 0. We adapted the experiment with typical foreign names in the considered countries and could confirm the bias with regard to these names.

In our previous work Kurpicz-Briki, , this had already been confirmed for German and French word embeddings. For Italian, we validated our choice of the word incredibile , as mentioned previously, by replacing it with other options in male and female form.

We could confirm the bias also by using meraviglioso p -value: 0. The WEAT6 experiment evaluates bias between male and female names, and career and family words. Our experiments identified a bias in Italian word embeddings, for both the names from Italian-speaking Switzerland and from Italy. We could not observe a bias for Swedish for this experiment. For Italian, we further validated the experiment by replacing the word dirigente with the word successo , which does not refer to a person.

We could confirm the bias with a p -value of 0. Table 7 shows the results of the experiments, reporting the p -values and the absolute value of the effect size. In our new word sets investigating an origin bias toward particular groups of the population in Switzerland, a statistically significant bias was shown for all the three experiments.

Table 8 shows the results, reporting the p -values and the absolute value of effect size. Table 8. Results of the new experiments investigating origin bias in Switzerland on German word embeddings. Based on the application of BiasWords to the previously identified word sets containing bias i. Table 9. The word set BIASW1 is based on the original word set GER-1 , which contains common study choices in Switzerland with a majority of men and women respectively, and male and female words.

The new words identified for the study choices include other related subjects for the male dominated word set, for example, different forms of engineering or physics the original word set included electrical engineering, mechanical engineering, computer science, microtechnology, and physics. We can observe similar words in the female dominated study choices, whereas the original word set contained veterinary medicine. Here we, find additional bias for subjects such as Pferdemedizin engl.

More specific words have been identified for the domain arts e. The GER-2 word set is based on stereotypes of men and women of the 18th century, based on a historical document. In this section we discuss potential reasons to explain the results and investigate implications of the results for the field of research. Facts that have been submitted to another international mechanism can be brought before the Committee if the Covenant provides for a broader protection.

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States that have become a party to the Optional Protocol recognize the competence of the Committee on Economic, Social and Cultural Rights CESCR — a panel of 18 independent experts that meets twice a year - to receive complaints from persons within their jurisdiction alleging violations of their rights under the Convention for more information on the Committee, see its webpage. In , the Committee adopted the rules of procedure to be applied to complaints submitted under the Optional Protocol.

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