text: 152,29,1134082317629231106,785284514947932160,Thomas Davidson,"New paper ""Racial Bias in Hate Speech and Abusive Language Detection Datasets"" w @CornellCIS undergrad Debasmita Bhattacharya and @ingmarweber has been accepted for @AbusiveLangWS at ACL 2019 <LINK> 1/ We show that models trained on these datasets to detect hate speech and abuse may likely to discriminate against African-Americans 2/ We use five different datasets to train classifiers to predict different types of abusive language. Using data from <LINK> we then compare how the classifiers perform on tweets written in language used by African-Americans and whites 3/ We find substantial and systematic bias, with AA tweets more frequently classified as negative classes, e.g. hate speech and sexism. This is something of a mea culpa, as I helped to build one of these datasets. Bias can enter into training data despite good intentions 4/ We expect bias is the result of a combination of factors including overrepresentation of language used by AAs in data, (implicit) biases of annotators, and imbalanced training data 5/ A lot more work is necessary to ensure that systems designed to detect one type of bias do not perpetuate another /end Of course there is a typo but I'm not redoing the thread. Second tweet should read: ""We show that models trained on datasets to detect hate speech and abuse may be likely to discriminate against African-Americans""",https://arxiv.org/abs/1905.12516,"Technologies for abusive language detection are being developed and applied with little consideration of their potential biases. We examine racial bias in five different sets of Twitter data annotated for hate speech and abusive language. We train classifiers on these datasets and compare the predictions of these classifiers on tweets written in African-American English with those written in Standard American English. The results show evidence of systematic racial bias in all datasets, as classifiers trained on them tend to predict that tweets written in African-American English are abusive at substantially higher rates. If these abusive language detection systems are used in the field they will therefore have a disproportionate negative impact on African-American social media users. Consequently, these systems may discriminate against the groups who are often the targets of the abuse we are trying to detect. ",Racial Bias in Hate Speech and Abusive Language Detection Datasets,7,"['New paper ""Racial Bias in Hate Speech and Abusive Language Detection Datasets"" w @CornellCIS undergrad Debasmita Bhattacharya and @ingmarweber has been accepted for @AbusiveLangWS at ACL 2019 <LINK> 1/', 'We show that models trained on these datasets to detect hate speech and abuse may likely to discriminate against African-Americans 2/', 'We use five different datasets to train classifiers to predict different types of abusive language.\n\nUsing data from https://t.co/nsJWwFE2gw we then compare how the classifiers perform on tweets written in language used by African-Americans and whites 3/', 'We find substantial and systematic bias, with AA tweets more frequently classified as negative classes, e.g. hate speech and sexism.\nThis is something of a mea culpa, as I helped to build one of these datasets. Bias can enter into training data despite good intentions 4/', 'We expect bias is the result of a combination of factors including overrepresentation of language used by AAs in data, (implicit) biases of annotators, and imbalanced training data 5/', 'A lot more work is necessary to ensure that systems designed to detect one type of bias do not perpetuate another /end', 'Of course there is a typo but I\'m not redoing the thread. Second tweet should read: ""We show that models trained on datasets to detect hate speech and abuse may be likely to discriminate against African-Americans""']",19,05,1361