The Lifecycle of “Facts”: A Survey of Social Bias in Knowledge Graphs (2022)

Abstract: Knowledge graphs are increasingly used in a plethora of downstream tasks or in the augmentation of statistical models to improve factuality. However, social biases are engraved in these representations and propagate downstream. We conducted a critical analysis of literature concerning biases at different steps of a knowledge graph lifecycle. We investigated factors introducing bias, as well as the biases that are rendered by knowledge graphs and their embedded versions afterward. Limitations of existing measurement and mitigation strategies are discussed and paths forward are proposed.

## introduction knowledge graphs (kgs) provide a structured and transparent form of information representation and lie at the core of popular semantic web technologies. they are utilized as a source of truth in a variety of downstream tasks (e.g., information extraction @xcite , link prediction @xcite , or question-answering @xcite @xcite ) and in hybrid ai systems (e.g., knowledge-augmented language models @xcite @xcite or conversational ai @xcite ). in the latter, kgs are employed to enhance the factuality of statistical models @xcite . in this overview article, we question the ethical integrity of these facts and investigate the lifecycle of kgs @xcite with respect to bias influences. 1 we claim that kgs manifest social biases and potentially propagate harmful prejudices. to uti1 we focus on the kg lifecycle from a bias and fairness lens. for reference, the processes investigated in section 3 correspond to the authoring stage in the taxonomy by @xcite . the representation issues in kgs (section 4) and kg embeddings (sections 5 and 7) which affect downstream task bias relate to auer et al.'s classification stage. lize the full potential of kg technologies, such ethical risks must be targeted and avoided during development and application. using an extensive literature analysis, this article provides a reflection on previous efforts and suggestions for future work. we collected articles via google scholar foot_0 and filtered for titles including knowledge graph/base/resource, ontologies, named entity recognition, or relation extraction, paired with variants of bias, debiasing, harms, ethical, and fairness. we selected peer-reviewed publications (in journals, conference or workshop proceedings, and book chapters) from 2010 onward, related to social bias in the kg lifecycle. this resulted in a final count of 18 papers. ## notes on bias, fairness, and factuality in the following, we clarify our operational definitions of the most relevant concepts in our analysis. ## entering the lifecycle: bias in knowledge graph creation we enter the lifecycle view (figure 1 ) by investigating the processes underlying the creation of kgs. we focus on the human factors behind the authoring of ontologies and triples which constitute kgs. furthermore, we address automated information extraction, i.e., the detection and extraction of entities and relations from text, since these approaches can be subject to algorithmic bias. ## bias in knowledge graphs next in our investigation of the lifecycle (figure 1 ) comes the representation of entities and relations as a kg. in the following, we illustrate which social biases are manifested in kgs and how. janowicz et al. (2018) demonstrated that dbpedia, which is sourced from wikipedia info boxes, mostly represents the western and industrialized world. matching the coverage of location entries in the kg with population density all over the world showed that several countries and continents are underrepresented. a disproportionate 70% of the person entities in wikidata are male (20% are female, less than 1% are neither male nor female, and for roughly 10% the gender is not indicated) @xcite . @xcite found that the most frequent occupation is researcher and beytía et al. (2022) identified arts, sports, and science and technology as the most prominent occupation categories. in reality, only about 2% of people in the u.s. are researchers @xcite . this gap is likely caused by reporting bias as discussed earlier (section 3.3). radstok et al. (2021), moreover, observed that mentions of ethnic group membership decreased and changed in focus between the 18th and 21st century. greeks are the most frequently labeled ethnic group among historic entries (over 400 times) and african americans among modern entries (only roughly 100 times). ## bias in knowledge graph embeddings vector representations of kgs are used in a range of downstream tasks or combined with other types of neural models @xcite . they facilitate efficient aggregation of connectivity patterns and convey latent information. embeddings are created through statistical modeling and summarize distributional characteristics. so, if a kg like wikidata contains mostly (if not only) male presidents, the relationship between the gender male and the profession president is assumed to manifest itself accordingly in the model. in fact, the papers summarized below provide evidence that the social biases of kgs are modeled or further amplified by kg embeddings (kges). the following sections are organized by measurement strategy to give an overview of existing approaches and the information gained from them. ## breaking the cycle? bias mitigation in knowledge graph embeddings a number of works have attempted to post-hoc mitigate biases in kges. given that pre-existing biases are hard to eradicate from kgs, manipulating embedding procedures, may alleviate the issue at least on a representation level. in the following, we summarize respective approaches. ## discussion in this article, we cover a wide range of evidence for harmful biases at different stages during the lifecycle of "facts" as represented in kgs. some of the most influential graphs misrepresent the world as it is due to sampling and algorithmic biases at the creation step. pre-existing biases are exaggerated in these representations. embedding models learn to encode the same or further amplified versions of these biases. since the training of high-quality embeddings is costly, they are, in practice, pretrained once and afterward reused and fine-tuned for different systems. these systems preserve the inherited biases over long periods, exacerbating the issue further. our survey shows that kgs may qualify as resources for historic facts, but they do not qualify for inference regarding various human attributes. future work on biases in kgs and kges should aim for improvement in the following areas: attribute and seed choices bias metrics usually examine one or a few specific attributes (e.g., occupation) and their correlations with selected seed dimensions (e.g., gender). occupation is by far the most researched attribute in the articles we found @xcite @xcite . only @xcite propose to aggregate the correlations between a set of seed dimensions and all relations in a graph. all the works used binary gender as the seed dimension and some additionally addressed ethnicity, religion, and nationality @xcite . lack of validation most of the kge bias metrics presented here are interpreted as valid if they detect unfairly discriminating association patterns that intuitively align with existing stereotypes. besides that, several works investigate the comparability between different metrics. although both of these practices deliver valuable information on validity, they largely ignore the societal context. only @xcite compared embedding-level bias metrics with census-aligned data to assess compatibility with real-world inequalities. we suggest that future work consider a more comprehensive study of construct validity (does the measurement instrument measure the construct in a meaningful and useful capacity?) @xcite . one requirement is that the obtained measurements capture all relevant aspects of the construct the instrument claims to measure. that is, a gender bias measure must measure all relevant aspects of gender bias @xcite (including, e.g., nonbinary gender and a distinction between benevolent and hostile forms of sexist stereotyping @xcite ). unless proven otherwise, we must be skeptical that this is achieved by existing approaches @xcite . as a result of minimal validation, detailed interpretation guidelines are generally not provided. therefore, the distinctions between strong and weak bias or weak bias and random variation are mostly vague. ## recommendations to avoid harms caused by biases in kgs and their embeddings, we identify and recommend several actions for practitioners and researchers. transparency and accountability kgs should by default be published with bias-sensitive documentation to facilitate transparency and accountability regarding potential risks. data statements @xcite report curation criteria, language variety, demographics of the data authors and annotators, relevant indicators of context, quality, and provenance. datasheets for datasets @xcite additionally state motivation, composition, preparation, distribution, and maintenance. the associated questionnaire can accompany the dataset creation process to avoid risks early on. especially in the case of ongoing crowdsourcing efforts for encyclopedic kgs the demographic background of contributors should be reported @xcite . researchers using subsets of these kgs, should investigate respective data dumps for potential biases and report limitations transparently. similarly, kg embedding models should be published with model cards @xcite documenting intended use, underlying data, ethical considerations, and limitations. stating the contact details for reporting problems and concerns establishes accountability @xcite . improving representativeness to tackle selection bias, data collection should aim to employ authors and annotators from diverse social groups and with varied cultural imprints. annotations should be determined via aggregation (see @xcite . for open editable kgs, interventions like edit-a-thons are helpful to introduce more authors from underrepresented groups @xcite (e.g., the art+feminism campaign aims to fill the gender gap in wikimedia knowledge bases foot_6 ). in order for such interventions to take effect, research must update data bases and benchmarks frequently (see @xcite . in addition, the timeliness of encyclopedic data is necessary to avoid perpetuating historic biases. tackling algorithmic bias evaluation and prevention of harmful biases must become part of the development pipeline (stanczak and augenstein, 647 2021). algorithmic biases are best evaluated with a combination of multiple quantitative (section 5) and qualitative measures @xcite , considering multiple demographic dimensions (beyond gender and occupation). evaluating the content of attributions in light of social discourse and the intended use of a technology facilitates an assessment of potential harms @xcite . downstream task bias may exist independently from a measured embedding bias @xcite , therefore a taskand context-oriented evaluation is preferred (section 6). we have presented several bias-mitigating strategies for different kges, which might alleviate the issue in some cases (section 7). however, more research is needed to establish more effective and robust mitigation methods, as well as metrics used to evaluate their impact @xcite . ## related work although a wide range of surveys investigates biases in nlp, none of them addresses kg-based methods, in particular. blodgett et al. (2020) critically investigated the theoretical foundation of works analyzing bias in nlp. the authors claim that most works lack a clear taxonomy. we came to a similar conclusion with respect to evaluations of kgs and their embeddings. @xcite and @xcite surveyed algorithmic measurement and mitigation strategies for gender bias in nlp. @xcite summarized approaches for the measurement and mitigation of bias in generative language models. some of the methods presented earlier are derived from works discussed in these surveys and adapted to the constraints of kg embeddings (e.g., bourli and pitoura (2020) adapted hard debiasing @xcite ). criticisms point to the monolingual focus on the english language, the predominant assumption of a gender binary, and a lack of interdisciplinary collaboration. @xcite identified four sources of predictive biases: label bias (label distributions are imbalanced and erroneous regarding certain demographics), selection bias (the data sample is not representative of the real world distribution), semantic bias/input representation bias (e.g., feature creation with biased embeddings), and overamplification through the predictive model (slight differences between human attributes are overempha-sized by the model). all of these factors are reflected in the lifecycle as discussed in this article. to counter the risks, @xcite suggest employing multiple annotators and methods of aggregation (see also @xcite , re-stratification, re-weighting, or data augmentation, debiasing of models, and, finally, standardized data and model documentation. ## conclusion and paths forward our survey shows that biases affect kgs at different stages of their lifecycle. social biases enter kgs in various ways at the creation step (e.g., through crowd-sourcing of triples and ontologies) and manifest in popular graphs, like dbpedia @xcite or conceptnet @xcite . embedding models can capture exaggerated versions of these biases @xcite , which finally propagate downstream @xcite . we acknowledge that kgs have enormous potential for a variety of knowledge-driven downstream applications @xcite @xcite and improvements in the truthfulness of statistical models @xcite ). yet, although kgs are factual about historic instances, they also perpetuate historically emerging social inequalities. thus, ethical implications must be considered when developing or reusing these technologies. we showed that most embedding-based measurement approaches for bias are still restricted to a limited number of demographic seeds and attributes. furthermore, their alignment with social bias as a construct is not sufficiently validated. some debiasing strategies appear effective within rather narrow definitions of bias. more in-depth scrutiny is required for a broader understanding of bias. future work should be grounded in an investigation of concepts like gender or ethnic bias and strive for more comprehensive operationalizations and validation studies. finally, the motivations and conceptualizations should be communicated clearly.