In this paper we explore quarantining as a more ethical method for delimiting the spread of Hate Speech via online social media platforms. Currently, companies like Facebook, Twitter, and Google generally respondreactivelyto such material: offensive messages that have already been posted are reviewed by human moderators if complaints from users are received. The offensive posts are onlysubsequentlyremoved if the complaints are upheld; therefore, they still cause the recipients psychological harm. In addition, this approach has frequently been criticised for delimiting freedom (...) of expression, since it requires the service providers to elaborate and implement censorship regimes. In the last few years, an emerging generation of automatic Hate Speech detection systems has started to offer new strategies for dealing with this particular kind of offensive online material. Anticipating the future efficacy of such systems, the present article advocates an approach to online Hate Speech detection that is analogous to the quarantining of malicious computer software. If a given post is automatically classified as being harmful in a reliable manner, then it can be temporarily quarantined, and the direct recipients can receive an alert, which protects them from the harmful content in the first instance. The quarantining framework is an example of more ethical online safety technology that can be extended to the handling of Hate Speech. Crucially, it provides flexible options for obtaining a more justifiable balance between freedom of expression and appropriate censorship. (shrink)

This article probes the practical ethical implications of AI system design by reconsidering the important topic of bias in the datasets used to train autonomous intelligent systems. The discussion draws on recent work concerning behaviour-guiding technologies, and it adopts a cautious form of technological utopianism by assuming it is potentially beneficial for society at large if AI systems are designed to be comparatively free from the biases that characterise human behaviour. However, the argument presented here critiques the common well-intentioned requirement (...) that, in order to achieve this, all such datasets must be debiased prior to training. By focusing specifically on gender-bias in Neural Machine Translation systems, three automated strategies for the removal of bias are considered – downsampling, upsampling, and counterfactual augmentation – and it is shown that systems trained on datasets debiased using these approaches all achieve general translation performance that is much worse than a baseline system. In addition, most of them also achieve worse performance in relation to metrics that quantify the degree of gender bias in the system outputs. By contrast, it is shown that the technique of domain adaptation can be effectively deployed to debias existing NMT systems after they have been fully trained. This enables them to produce translations that are quantitatively far less biased when analysed using gender-based metrics, but which also achieve state-of-the-art general performance. It is hoped that the discussion presented here will reinvigorate ongoing debates about how and why bias can be most effectively reduced in state-of-the-art AI systems. (shrink)

The events surrounding the ‘Arab Spring’ have attracted an enormous amount of attention by the international press as well as on social media platforms, especially in its initial phase in early 2011. This article investigates how violent and forceful actions during the ‘Arab Revolutions’ were conceptualised linguistically by incorporating notions of Cognitive Semantics in a critical comparative study of press reports and Twitter posts. Focus is placed specifically on combining Talmy’s theory of Force Dynamics with methods of Critical Discourse Studies (...) in order to investigate diverging forms of schematisation of forceful interactions among protest participants. The most frequently recurring opposing force-dynamic patterns identified are defying a hindrance, prevailing stability and causation of action as well as inaction. Notable differences may be observed between mass media and social media in regard to the representation of dynamic power relations between police and protesters. (shrink)

This volume looks at the forms and functions of counterspeech as well as what determines its effectiveness and success from multidisciplinary perspectives. Counterspeech is in line with international human rights and freedom of speech and it can be a much more powerful tool against dangerous and toxic speech than blocking and censorship. In the face of online hate speech and disinformation, counterspeech is a tremendously important and timely topic. The book uniquely brings together expertise from a variety of disciplines. It (...) explores linguistic, ethical and legal aspects of counterspeech, looks at the functions and effectiveness of counterspeech from anthropological, practical and sociological perspectives and addresses the question of how we can use modern technological advances to make counterspeech a more instantaneous and efficient option to respond to harmful language online. The greatest benefit of counterspeech lies in the ability to reach bystanders and prevent them from becoming perpetrators themselves. This volume is an excellent opportunity to spread the word about counterspeech, its potential, importance, and future endeavors. This anthology is a great resource for scholars and students of linguistics, philosophy of language, media and communication studies, digital humanities, natural language processing, international human rights law, anthropology and sociology, and interdisciplinary research methods. It is also a valuable source of information for practitioners and anyone who wants to speak up against harmful speech. (shrink)