A human-in-the-loop framework to handle implicit bias in crowdsourced KGs

Morales Tirado, Alba; Oelen, Allard; Pasqual, Valentina; Shi, Meilin; Umbrico, Alessandro; Xu, Weiqin and Celino, Irene (2020). A human-in-the-loop framework to handle implicit bias in crowdsourced KGs. In: Knowledge Graphs Evolution andPreservation - A Technical Report from ISWS 2019, pp. 101–108.

URL: https://arxiv.org/pdf/2012.11936.pdf

Abstract

Crowd-sourced Knowledge Graphs (KGs) may be biased: some biases can originate from factual errors, while others reflect different points of view. How to identify and measure biases in crowd-sourced KGs? And then, how to tell apart factual errors from different point of views? And how to put together all these steps contextualized in a human-in-the-loop framework?

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