Leveraging Ontologies to Document Bias in Data

TL;DR

This paper introduces the Doc-BiasO ontology, a formal resource to describe and measure biases in machine learning pipelines, aiming to improve understanding and documentation of bias in AI systems.

Contribution

The paper presents the development of the Doc-BiasO ontology, integrating bias definitions, measures, and related terminology to enhance bias documentation and interoperability in AI research.

Findings

Created an ontology for bias in ML pipelines

Reused existing vocabularies for interoperability

Aims to clarify bias terminology in AI

Abstract

Machine Learning (ML) systems are capable of reproducing and often amplifying undesired biases. This puts emphasis on the importance of operating under practices that enable the study and understanding of the intrinsic characteristics of ML pipelines, prompting the emergence of documentation frameworks with the idea that ``any remedy for bias starts with awareness of its existence''. However, a resource that can formally describe these pipelines in terms of biases detected is still amiss. To fill this gap, we present the Doc-BiasO ontology, a resource that aims to create an integrated vocabulary of biases defined in the \textit{fair-ML} literature and their measures, as well as to incorporate relevant terminology and the relationships between them. Overseeing ontology engineering best practices, we re-use existing vocabulary on machine learning and AI, to foster knowledge sharing and interoperability between the actors concerned with its research, development, regulation, among others. Overall, our main objective is to contribute towards clarifying existing terminology on bias research as it rapidly expands to all areas of AI and to improve the interpretation of bias in data and downstream impact.