Doc-Bias KG

A hybrid AI system capable of identifying bias patterns in datasets used by predictive AI models. The proposed system captures knowledge about dataset characteristics and represents it as factual statements in a knowledge graph.

The proposed hybrid AI system is used to trace the PSL implementation of the AI model for fake news detection (https://doi.org/10.1145/3340531.3412066).

This repository contains following contents:

1. DataSources: The data files generated to trace the fake news model and dataset defined in: https://github.com/linqs/chowdhury-cikm20.
2. Doc-BiasKG: schema, mapping rules, resulting experiments.
3. Folder sparql_queries contains all the necessary bias analysis performed over the generated Doc-Bias KG.
4. DocBias_over_InterpretME: Doc-Bias analysis applied over a principled approach, InterpretME. The necessary instructions for execution of InterpretME pipeline and running sparql queries over the InterpretME KG is available in this folder.

Getting Started

Clone the repository

git clone git@github.com:SDM-TIB/DocBiasKG.git

Running Doc Bias

Install necessary dependencies for Doc-Bias approach, create a virtual environment and run the following command:

pip install -r requirements.txt

References

[1] Marco Ribeiro, Sameer Singh, and Carlos Guestrin. "Why Should I Trust You?": Explaining the Predictions of Any Classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '16). ACM. 2016. DOI: 10.1145/2939672.2939778

[2] Yashrajsinh Chudasama, Disha Purohit, Philipp D. Rohde, Julian Gercke, Maria-Esther Vidal. "InterpretME: A Tool for Interpretations of Machine Learning Models Over Knowledge Graphs" In Semantic Web Journal, Special Issue on Tools & Systems. DOI: 10.3233/SW-233511