  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.
