# Bias in ontologies -- a preliminary assessment

arXiv: https://arxiv.org/abs/2101.08035 · HTML: https://mlsanity.com/paper/2101.08035/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 38 references that are in the map.
- First posted on arXiv 20 Jan 2021.

## Builds on (most cited first, 2 of 2)

- [CODO: An Ontology for Collection and Analysis of Covid-19 Data](https://mlsanity.com/paper/2009.01210/) (2020)
- [Toward equipping Artificial Moral Agents with multiple ethical theories](https://mlsanity.com/paper/2003.00935/) (2020)

## Abstract (arXiv)

Logical theories in the form of ontologies and similar artefacts in computing and IT are used for structuring, annotating, and querying data, among others, and therewith influence data analytics regarding what is fed into the algorithms. Algorithmic bias is a well-known notion, but what does bias mean in the context of ontologies that provide a structuring mechanism for an algorithm's input? What are the sources of bias there and how would they manifest themselves in ontologies? We examine and enumerate types of bias relevant for ontologies, and whether they are explicit or implicit. These eight types are illustrated with examples from extant production-level ontologies and samples from the literature. We then assessed three concurrently developed COVID-19 ontologies on bias and detected different subsets of types of bias in each one, to a greater or lesser extent. This first characterisation aims contribute to a sensitisation of ethical aspects of ontologies primarily regarding representation of information and knowledge.