Conference Paper

Identifying Biases in Politically Biased Wikis through Word Embeddings

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Abstract

With the increase of biased information available online, the importance of analysis and detection of such content has also significantly risen. In this paper, we aim to quantify different kinds of social biases using word embeddings. Towards this goal we train such embeddings on two politically biased MediaWiki instances, namely RationalWiki and Conservapedia. Additionally we included Wikipedia as an online encyclopedia, which is accepted by the general public. Utilizing and combining state-of-the-art word embedding models with WEAT and WEFAT, we display to what extent biases exist in the above-mentioned corpora. By comparing embeddings we observe interesting differences between different kinds of wikis.

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... This impacts generalization performance negatively (Shah et al., 2020) and may have harmful consequences in practical applications (Bender et al., 2021;Joseph and Morgan, 2020). So far, one hurdle to mitigate these problems is the limited reliability of common measures of social bias present in a corpus (Spliethöver and Wachsmuth, 2021), stemming from embedding training algorithms not tailored to low-resource situations (Knoche et al., 2019;Spinde et al., 2021). ...
... WEAT's main idea is to calculate the cumulative distance between groups of word vectors that describe a social group and attributes. Similar measures exist, such as ECT (Dev and Phillips, 2019), RNSB (Sweeney and Najafian, 2019), and MAC (Manzini et al., 2019), RIPA (Ethayarajh et al., 2019), WEATVEC (Knoche et al., 2019), the Smoothed First-Order Co-occurrence (Rekabsaz et al., 2021) and SAME (Schröder et al., 2021) but our goal is not to find the best measure. Rather, we seek to learn how measures like WEAT behave for different embedding algorithms. ...
... Closest to our work is the research of Knoche et al. (2019) and Spinde et al. (2021). The former use WEAT to compare social biases present word embeddings trained on different ideological online wikis. ...
Preprint
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News articles both shape and reflect public opinion across the political spectrum. Analyzing them for social bias can thus provide valuable insights, such as prevailing stereotypes in society and the media, which are often adopted by NLP models trained on respective data. Recent work has relied on word embedding bias measures, such as WEAT. However, several representation issues of embeddings can harm the measures' accuracy, including low-resource settings and token frequency differences. In this work, we study what kind of embedding algorithm serves best to accurately measure types of social bias known to exist in US online news articles. To cover the whole spectrum of political bias in the US, we collect 500k articles and review psychology literature with respect to expected social bias. We then quantify social bias using WEAT along with embedding algorithms that account for the aforementioned issues. We compare how models trained with the algorithms on news articles represent the expected social bias. Our results suggest that the standard way to quantify bias does not align well with knowledge from psychology. While the proposed algorithms reduce the~gap, they still do not fully match the literature.
... Other authors have previously researched both how algorithms can be investigated for journalistic purposes (Diakopoulos, 2015), described how algorithms involved in newswork could be made transparent (Diakopoulos & Koliska, 2017) and provided descriptions of how automation can help reduce bias in reporting (Fischer-Hwang, Grosz, Hu, Karthik, & Yang, 2020). Similarly, some technical works have investigated methods for identifying bias in non-journalistic contexts (e.g., Caliskan, Bryson, & Narayanan, 2017;Knoche, Popović, Lemmerich, & Strohmaier, 2019). In this article, we synthesize how these methods and ideas apply to diagnosing automated journalism itself for bias. ...
... Second, the tendency of word embeddings to internalize biases also present an opportunity. Previous works (e.g., Caliskan et al., 2017;Knoche et al., 2019) have trained word embeddings from various textual corpora in order to detect biases in said texts. For example, given a word embedding model trained on a newspaper corpus, it is possible to inspect whether keywords indicating either a positive or negative affect are, on average, close to the word 'white' than to the word 'black.' ...
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In this article we consider automated journalism from the perspective of bias in news text. We describe how systems for automated journalism could be biased in terms of both the information content and the lexical choices in the text, and what mechanisms allow human biases to affect automated journalism even if the data the system operates on is considered neutral. Hence, we sketch out three distinct scenarios differentiated by the technical transparency of the systems and the level of cooperation of the system operator, affecting the choice of methods for investigating bias. We identify methods for diagnostics in each of the scenarios and note that one of the scenarios is largely identical to investigating bias in non-automatically produced texts. As a solution to this last scenario, we suggest the construction of a simple news generation system, which could enable a type of analysis-by-proxy. Instead of analyzing the system, to which the access is limited, one would generate an approximation of the system which can be accessed and analyzed freely. If successful, this method could also be applied to analysis of human-written texts. This would make automated journalism not only a target of bias diagnostics, but also a diagnostic device for identifying bias in human-written news.
... Datasets: Extending the experimental design from [8], we apply debiasing simultaneously on following target sets/subclasses: (male, female) -gender, (islam, christianity, atheism) -religion and (black and white names) -race with seven distinct attribute set pairs 5 . We collected target, attribute sets, and class definitional sets from literature [11,16,15,3,10,8], see our online appendix for a complete list. ...
... Datasets: Extending the experimental design from [8], we apply debiasing simultaneously on following target sets/subclasses: (male, female) -gender, (islam, christianity, atheism) -religion and (black and white names) -race with seven distinct attribute set pairs 5 . We collected target, attribute sets, and class definitional sets from literature [11,16,15,3,10,8], see our online appendix for a complete list. As in previous studies [7], evaluation was done on three pretrained Word Embedding models with vector dimension of 300: FastText 2 (English webcrawl and Wikipedia, 2 million words), GloVe 3 (Common Crawl, Wikipedia and Gigaword, 2.2 million words) and Word2Vec 4 (Trained on Google News, 3 million words). ...
Preprint
Bias in Word Embeddings has been a subject of recent interest, along with efforts for its reduction. Current approaches show promising progress towards debiasing single bias dimensions such as gender or race. In this paper, we present a joint multiclass debiasing approach that is capable of debiasing multiple bias dimensions simultaneously. In that direction, we present two approaches, HardWEAT and SoftWEAT, that aim to reduce biases by minimizing the scores of the Word Embeddings Association Test (WEAT). We demonstrate the viability of our methods by debiasing Word Embeddings on three classes of biases (religion, gender and race) in three different publicly available word embeddings and show that our concepts can both reduce or even completely eliminate bias, while maintaining meaningful relationships between vectors in word embeddings. Our work strengthens the foundation for more unbiased neural representations of textual data.
... Other highprofile papers such as Garg et al. (2018) and Lewis and Lupyan (2020) have used the WEAT to study cultural biases across time and place. Importantly, the method is now being used to evaluate the political biases of websites (Knoche et al. 2019), detect the purposeful spread of misinformation on social media by state-sponsored actors (Toney et al. 2021), uncover biases present and proliferated through popular song lyrics (Barman, Awekar, and Kothari 2019), and even to measure how much gender bias US judges display in their judicial opinions (Ash, Chen, and Galletta 2021). ...
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The word embedding association test (WEAT) is an important method for measuring linguistic biases against social groups such as ethnic minorities in large text corpora. It does so by comparing the semantic relatedness of words prototypical of the groups (e.g., names unique to those groups) and attribute words (e.g., 'pleasant' and 'unpleasant' words). We show that anti-black WEAT estimates from geo-tagged social media data at the level of metropolitan statistical areas strongly correlate with several measures of racial animus--even when controlling for sociodemographic covariates. However, we also show that every one of these correlations is explained by a third variable: the frequency of Black names in the underlying corpora relative to White names. This occurs because word embeddings tend to group positive (negative) words and frequent (rare) words together in the estimated semantic space. As the frequency of Black names on social media is strongly correlated with Black Americans' prevalence in the population, this results in spurious anti-Black WEAT estimates wherever few Black Americans live. This suggests that research using the WEAT to measure bias should consider term frequency, and also demonstrates the potential consequences of using black-box models like word embeddings to study human cognition and behavior.
... One of the additional lexicons tested, the WEAT lexicon, deserves special consideration since previous works have used this small size lexicon (N = 50) when testing for bias in word embedding models [8,15,16]. Although results of projecting WEAT sentiment words onto the cultural axes analyzed roughly agree with the HGI lexicon projection tests, some cultural axes show divergent results. ...
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Concerns about gender bias in word embedding models have captured substantial attention in the algorithmic bias research literature. Other bias types however have received lesser amounts of scrutiny. This work describes a large-scale analysis of sentiment associations in popular word embedding models along the lines of gender and ethnicity but also along the less frequently studied dimensions of socioeconomic status, age, physical appearance, sexual orientation, religious sentiment and political leanings. Consistent with previous scholarly literature, this work has found systemic bias against given names popular among African-Americans in most embedding models examined. Gender bias in embedding models however appears to be multifaceted and often reversed in polarity to what has been regularly reported. Interestingly, using the common operationalization of the term bias in the fairness literature, novel types of so far unreported bias types in word embedding models have also been identified. Specifically, the popular embedding models analyzed here display negative biases against middle and working-class socioeconomic status, male children, senior citizens, plain physical appearance and intellectual phenomena such as Islamic religious faith, non-religiosity and conservative political orientation. Reasons for the paradoxical underreporting of these bias types in the relevant literature are probably manifold but widely held blind spots when searching for algorithmic bias and a lack of widespread technical jargon to unambiguously describe a variety of algorithmic associations could conceivably be playing a role. The causal origins for the multiplicity of loaded associations attached to distinct demographic groups within embedding models are often unclear but the heterogeneity of said associations and their potential multifactorial roots raises doubts about the validity of grouping them all under the umbrella term bias. Richer and more fine-grained terminology as well as a more comprehensive exploration of the bias landscape could help the fairness epistemic community to characterize and neutralize algorithmic discrimination more efficiently.
... One of the additional lexicons tested, the WEAT lexicon, deserves special consideration since previous works have used this small size lexicon (N=50) when testing for bias in word embedding models (8,15,16). Although results of projecting WEAT sentiment words onto the cultural axes analyzed roughly agree with the HGI lexicon projection tests, some cultural axes show divergent results. ...
Preprint
Full-text available
Concerns about gender bias in word embedding models have captured substantial attention in the algorithmic bias research literature. Yet, the common elastic usage of the term bias to describe a broad array of distinct algorithmic phenomena can be misleading. Here, a large-scale analysis of gender associations in popular pre-trained word embedding models suggests that the purported gender bias in these models is multifaceted and often reversed in polarity to what has been regularly reported. Consistent with previous scholarly literature, this work has found bias against given names popular among African-Americans in all embedding models examined. Interestingly, using this popular operationalization of the term bias in the fairness literature, novel types of so far unreported bias types in word embedding models have also been identified. Specifically, biases against middle and working class socioeconomic status, male children, senior citizens and intellectual phenomena such as Islamic religious faith and conservative political orientation. Still, using the umbrella term bias to refer to a heterogeneity of algorithmic associations in language models precludes precise characterization of algorithmic unfairness or lack thereof. Richer and more precise terminology could help the field to communicate more efficiently.
Chapter
Bias in Word Embeddings has been a subject of recent interest, along with efforts for its reduction. Current approaches show promising progress towards debiasing single bias dimensions such as gender or race. In this paper, we present a joint multiclass debiasing approach that is capable of debiasing multiple bias dimensions simultaneously. In that direction, we present two approaches, HardWEAT and SoftWEAT, that aim to reduce biases by minimizing the scores of the Word Embeddings Association Test (WEAT). We demonstrate the viability of our methods by debiasing Word Embeddings on three classes of biases (religion, gender and race) in three different publicly available word embeddings and show that our concepts can both reduce or even completely eliminate bias, while maintaining meaningful relationships between vectors in word embeddings. Our work strengthens the foundation for more unbiased neural representations of textual data.
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