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Named entity recognition systems perform well on standard datasets comprisingEnglish news. But given the paucity of data, it is difficult to drawconclusions about the robustness of systems with respect to recognizing adiverse set of entities. We propose a method for auditing the in-domainrobustness of systems, focusing specifically on differences in performance dueto the national origin of entities. We create entity-switched datasets, inwhich named entities in the original texts are replaced by plausible namedentities of the same type but of different national origin. We find thatstate-of-the-art systems' performance vary widely even in-domain: In the samecontext, entities from certain origins are more reliably recognized thanentities from elsewhere. Systems perform best on American and Indian entities,and worst on Vietnamese and Indonesian entities. This auditing approach canfacilitate the development of more robust named entity recognition systems, andwill allow research in this area to consider fairness criteria that havereceived heightened attention in other predictive technology work.

Entity-Switched Datasets: An Approach to Auditing the In-Domain Robustness of Named Entity Recognition Models