# Towards Automatic Bias Detection in Knowledge Graphs

arXiv: https://arxiv.org/abs/2109.10697 · HTML: https://mlsanity.com/paper/2109.10697/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 25 references that are in the map.
- First posted on arXiv 19 Sep 2021.

## Builds on (most cited first, 10 of 10)

- [Inherent Trade-Offs in the Fair Determination of Risk Scores](https://mlsanity.com/paper/1609.05807/) (2016)
- [ERNIE: Enhanced Language Representation with Informative Entities](https://mlsanity.com/paper/1905.07129/) (2019)
- [KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation](https://mlsanity.com/paper/1911.06136/) (2019)
- [Knowledge Enhanced Contextual Word Representations](https://mlsanity.com/paper/1909.04164/) (2019)
- [Compositional Fairness Constraints for Graph Embeddings](https://mlsanity.com/paper/1905.10674/) (2019)
- [PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings](https://mlsanity.com/paper/2007.14175/) (2020)
- [GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding](https://mlsanity.com/paper/1903.00757/) (2019)
- [Measuring Social Bias in Knowledge Graph Embeddings](https://mlsanity.com/paper/1912.02761/) (2019)
- [Adversarial Learning for Debiasing Knowledge Graph Embeddings](https://mlsanity.com/paper/2006.16309/) (2020)
- [The Impossibility Theorem of Machine Fairness -- A Causal Perspective](https://mlsanity.com/paper/2007.06024/) (2020)

## Built on by (most cited first, 6 of 6)

- [KALM: Knowledge-Aware Integration of Local, Document, and Global Contexts for Long Document Understanding](https://mlsanity.com/paper/2210.04105/) (2022)
- [The Lifecycle of "Facts": A Survey of Social Bias in Knowledge Graphs](https://mlsanity.com/paper/2210.03353/) (2022)
- [Social Biases in Knowledge Representations of Wikidata separates Global North from Global South](https://mlsanity.com/paper/2505.02352/) (2025)
- [Diversity matters: Robustness of bias measurements in Wikidata](https://mlsanity.com/paper/2302.14027/) (2023)
- [Benchmarking Fairness-aware Graph Neural Networks in Knowledge Graphs](https://mlsanity.com/paper/2510.18473/) (2025)
- [A Note on Bias to Complete](https://mlsanity.com/paper/2402.11710/) (2024)

## Abstract (arXiv)

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.