Proceedings of the KG-BIAS Workshop 2020 at AKBC 2020

TL;DR

The KG-BIAS 2020 workshop focused on identifying, measuring, and mitigating biases in knowledge graphs, including data source biases, language representation biases, and biases in personal KGs, to improve fairness and accuracy.

Contribution

This workshop consolidates research on biases in knowledge graphs, highlighting new methods for bias detection and remediation, and addressing biases in language and personal data sources.

Findings

Identification of various bias types in KGs

Development of bias measurement techniques

Proposals for bias mitigation strategies

Abstract

The KG-BIAS 2020 workshop touches on biases and how they surface in knowledge graphs (KGs), biases in the source data that is used to create KGs, methods for measuring or remediating bias in KGs, but also identifying other biases such as how and which languages are represented in automatically constructed KGs or how personal KGs might incur inherent biases. The goal of this workshop is to uncover how various types of biases are introduced into KGs, investigate how to measure, and propose methods to remediate them.