Implicit Bias in Crowdsourced Knowledge Graphs

By Gianluca Demartini. 2019

27 citations recorded by the source corpus.

Collaborative creation of knowledge is an approach which has been successfully demonstrated by crowdsourcing project like Wikipedia. Similar techniques have recently been adopted for the creation of collaboratively generated Knowledge Graphs like, for example, Wikidata. While such an approach enables the creation of high quality structured content, it also comes with the challenge of introducing contributors’ implicit bias in the generated Knowledge Graph. In this paper, we investigate how paid crowdsourcing can be used to understand contributor bias for controversial facts to be included into collaborative Knowledge Graphs. We propose methods to trace the provenance of crowdsourced fact checking thus enabling bias transparency rather than aiming at eliminating bias from the Knowledge Graph.

Subfields: Artificial Intelligence, Computer Science Applications, Management Science and Operations Research, Knowledge Graphs & Representation, AI Ethics, Fairness & Society

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In Artificial Intelligence

In Computer Science Applications

In Management Science and Operations Research

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In AI Ethics, Fairness & Society

Citations in the corpus

RuleBERT: Teaching Soft Rules to Pre-Trained Language Models (2021, methodology): “been used to mine the rules and compute their confidences (Janowicz et al., 2018; Demartini, 2019).”