DeepAI AI Chat
Log In Sign Up

Towards Automatic Bias Detection in Knowledge Graphs

by   Daphna Keidar, et al.
ETH Zurich
Stanford University

With the recent surge in social applications relying on knowledge graphs, the need for techniques to ensure fairness in KG based methods is becoming increasingly evident. Previous works have demonstrated that KGs are prone to various social biases, and have proposed multiple methods for debiasing them. However, in such studies, the focus has been on debiasing techniques, while the relations to be debiased are specified manually by the user. As manual specification is itself susceptible to human cognitive bias, there is a need for a system capable of quantifying and exposing biases, that can support more informed decisions on what to debias. To address this gap in the literature, we describe a framework for identifying biases present in knowledge graph embeddings, based on numerical bias metrics. We illustrate the framework with three different bias measures on the task of profession prediction, and it can be flexibly extended to further bias definitions and applications. The relations flagged as biased can then be handed to decision makers for judgement upon subsequent debiasing.


page 1

page 2

page 3

page 4


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

Knowledge graphs are increasingly used in a plethora of downstream tasks...

Diversity matters: Robustness of bias measurements in Wikidata

With the widespread use of knowledge graphs (KG) in various automated AI...

Causal foundations of bias, disparity and fairness

The study of biases, such as gender or racial biases, is an important to...

Bias in Conversational Search: The Double-Edged Sword of the Personalized Knowledge Graph

Conversational AI systems are being used in personal devices, providing ...

Knowledge Graphs and Machine Learning in biased C4I applications

This paper introduces our position on the critical issue of bias that re...

Adversarial Learning for Debiasing Knowledge Graph Embeddings

Knowledge Graphs (KG) are gaining increasing attention in both academia ...

Quantifying Social Biases Using Templates is Unreliable

Recently, there has been an increase in efforts to understand how large ...