Towards Engineering Fair Ontologies: Unbiasing a Surveillance Ontology

Abstract

Capturing knowledge in ontology-based AI applications may significantly propagate technical/statistical, cultural/social, cognitive/psychological, or other types of bias, to un-fair AI models and to their generated decisions. Biased ontologies (and consequently, knowledge graphs) engineered for intelligent surveillance applications can introduce technical barriers in fair capture of offenders, thus it must be researched as a first priority problem and a constant concern for explicit actions to be taken in the era of a more secure and fair world. In this paper we report preliminary research conducted on the novel topic of engineering fair ontologies and present first experiments with a prototype ontology and knowledge graph in the surveillance domain. Engineering fair ontologies is a quite new research topic, thus, the related work is at early stages. Having said that, in this paper we already highlight a recommended methodological approach for unbiasing ontologies, demonstrated in the surveillance domain, and we identify specific key research issues and challenges for further investigation by the ontology engineering community.