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

Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 639–652 November 20–23, 2022. ©2022 Association for Computational Linguistics 639 The Lifecycle of "Facts": A Survey of Social Bias in Knowledge Graphs Angelie Kraft1 and Ricardo Usbeck12 1Department of Informatics, Universität Hamburg, Germany 2Hamburger Informatik Technologie-Center e.V. (HITeC), Germany {angelie.kraft, ricardo.usbeck}@uni-hamburg.de Abstract Knowledge graphs are increasingly used in a plethora of downstream tasks or in the augmen- tation of statistical models to improve factuality. However, social biases are engraved in these representations and propagate downstream. We conducted a critical analysis of literature con- cerning biases at different steps of a knowl- edge graph lifecycle. We investigated factors introducing bias, as well as the biases that are rendered by knowledge graphs and their embed- ded versions afterward. Limitations of existing measurement and mitigation strategies are dis- cussed and paths forward are proposed. 1 Introduction Knowledge graphs (KGs) provide a structured and transparent form of information representation and lie at the core of popular Semantic Web technolo- gies. They are utilized as a source of truth in a variety of downstream tasks (e.g., information ex- traction (Martínez-Rodríguez et al., 2020), link pre- diction (Getoor and Taskar, 2007; Ngomo et al., 2021), or question-answering (Höffner et al., 2017; Diefenbach et al., 2018; Chakraborty et al., 2021; Jiang and Usbeck, 2022)) and in hybrid AI systems (e.g., knowledge-augmented language models (Pe- ters et al., 2019; Sun et al., 2020; Yu et al., 2022) or conversational AI (Gao et al., 2018; Gerritse et al., 2020)). In the latter, KGs are employed to enhance the factuality of statistical models (Athreya et al., 2018; Rony et al., 2022). In this overview article, we question the ethical integrity of these facts and investigate the lifecycle of KGs (Auer et al., 2012; Paulheim, 2017) with respect to bias influences.1 We claim that KGs manifest social biases and potentially propagate harmful prejudices. To uti- 1We 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 Auer et al. (2012). 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. Figure 1: Overview of the knowledge graph lifecycle as discussed in this paper. Exclamation marks indicate factors that introduce or amplify bias. We examine bias- inducing factors of triple crowd-sourcing, hand-crafted ontologies, and automated information extraction (Chap- ter 3), as well as the resulting social biases in KGs (Chapter 4) and KG embeddings, including approaches for measurement and mitigation (Chapter 5). 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 Scholar2 and filtered for …

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