Knowledge Graphs and Machine Learning in biased C4I applications

TL;DR

This paper discusses the critical issue of bias in AI applications within the C4I domain, focusing on the roles of Machine Learning and Knowledge Graphs, and proposes actions for debiasing in this security-sensitive area.

Contribution

It highlights the importance of addressing bias in C4I AI applications and calls for further research on debiasing methods within Knowledge Graph and Semantic Web communities.

Findings

Bias is more critical in C4I due to security implications

Current AI technologies contribute to bias in C4I applications

Proposed actions aim to mitigate bias in future C4I AI systems

Abstract

This paper introduces our position on the critical issue of bias that recently appeared in AI applications. Specifically, we discuss the combination of current technologies used in AI applications i.e., Machine Learning and Knowledge Graphs, and point to their involvement in (de)biased applications of the C4I domain. Although this is a wider problem that currently emerges from different application domains, bias appears more critical in C4I than in others due to its security-related nature. While proposing certain actions to be taken towards debiasing C4I applications, we acknowledge the immature aspect of this topic within the Knowledge Graph and Semantic Web communities.