The language of discrimination: assessing attention discrimination by Hungarian local governments

Abstract

In our study we assess the responsiveness of Hungarian local governments to requests for information by Roma and non-Roma clients, relying on a nationwide correspondence study. Our paper has both methodological and substantive relevance. The methodological novelty is that we treat discrimination as a classification problem and study to what extent emails written to Roma and non-Roma clients can be distinguished, which in turn serves as a metric of discrimination in general. We show that it is possible to detect discrimination in textual data in an automated way without human coding, and that machine learning (ML) may detect features of discrimination that human coders may not recognize. To the best of our knowledge, our study is the first attempt to assess discrimination using ML techniques. From a substantive point of view, our study focuses on linguistic features the algorithm detects behind the discrimination. Our models worked significantly better compared to random classification (the accuracy of the best of our models was 61%), confirming the differential treatment of Roma clients. The most important predictors showed that the answers sent to ostensibly Roma clients are not only shorter, but their tone is less polite and more reserved, supporting the idea of attention discrimination, in line with the results of Bartos et al. (2016). A higher level of attention discrimination is detectable against male senders, and in smaller settlements. Also, our results can be interpreted as digital discrimination in the sense in which Edelman and Luca (2014) use this term.