An Ontology for Fairness Metrics
AuthorFranklin, Jade S; Bhanot, Karan; Ghalwash, Mohamed; Bennett, Kristin P.; McCusker, Jamie; McGuinness, Deborah L.
Full CitationJade S. Franklin, Karan Bhanot, Mohamed Ghalwash, Kristin P. Bennett, Jamie McCusker, and Deborah L. McGuinness. 2022. An Ontology for Fairness Metrics. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES '22). Association for Computing Machinery, New York, NY, USA, 265–275. https://doi.org/10.1145/3514094.3534137
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URIhttps://dl.acm.org/doi/10.1145/3514094.3534137; https://doi.org/10.1145/3514094.3534137; https://hdl.handle.net/20.500.13015/6435
AbstractRecent research has revealed that many machine-learning models and the datasets they are trained on suffer from various forms of bias, and a large number of different fairness metrics have been created to measure this bias. However, determining which metrics to use, as well as interpreting their results, is difficult for a non-expert due to a lack of clear guidance and issues of ambiguity or alternate naming schemes between different research papers. To address this knowledge gap, we present the Fairness Metrics Ontology (FMO), a comprehensive and extensible knowledge resource that defines each fairness metric, describes their use cases, and details the relationships between them. We include additional concepts related to fairness and machine learning models, enabling the representation of specific fairness information within a resource description framework (RDF) knowledge graph. We evaluate the ontology by examining the process of how reasoning-based queries to the ontology were used to guide the fairness metric-based evaluation of a synthetic data model.;
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