An Ontology for Reasoning About Fairness in Regression and Machine Learning
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Authors
Franklin, Jade S.
Powers, Hannah
Erickson, John S.
McCusker, Jamie
McGuinness, Deborah L.
Bennett, Kristin P.
Issue Date
2023-10-31
Type
Article
Language
Keywords
ontology , fairness , metrics , regression , reasoning , SPARQL , OWL , machine learning
Alternative Title
Abstract
As concerns have grown about bias in ML models, the field of ML fairness has expanded considerably beyond classification. Researchers now propose fairness metrics for regression, but unlike classification there is no literature review of regression fairness metrics and no comprehensive resource to define, categorize, and compare them. To address this, we have surveyed the field, categorized metrics according to which notion of fairness they measure, and integrated them into an OWL2 ontology for fair regression extending our previously-developed ontology for reasoning about concepts in fair classification. We demonstrate its usage through an interactive web application that dynamically builds SPARQL queries to display fairness metrics meeting users’ selected requirements. Through this research, we provide a resource intended to support fairness researchers and model developers alike, and demonstrate methods of making an ontology accessible to users who may be unfamiliar with background knowledge of its domain and/or ontologies themselves.
Description
Full Citation
Franklin, Jade S., Hannah Powers, John S. Erickson, Jamie McCusker, Deborah L. McGuinness, and Kristin P. Bennett. "An Ontology for Reasoning About Fairness in Regression and Machine Learning." In Iberoamerican Knowledge Graphs and Semantic Web Conference, pp. 243-261. Cham: Springer Nature Switzerland, 2023.
Publisher
Springer Nature Switzerland