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The pervasiveness of abusive content on the internet can lead to severepsychological and physical harm. Significant effort in Natural LanguageProcessing (NLP) research has been devoted to addressing this problem throughabusive content detection and related sub-areas, such as the detection of hatespeech, toxicity, cyberbullying, etc. Although current technologies achievehigh classification performance in research studies, it has been observed thatthe real-life application of this technology can cause unintended harms, suchas the silencing of under-represented groups. We review a large body of NLPresearch on automatic abuse detection with a new focus on ethical challenges,organized around eight established ethical principles: privacy, accountability,safety and security, transparency and explainability, fairness andnon-discrimination, human control of technology, professional responsibility,and promotion of human values. In many cases, these principles relate not onlyto situational ethical codes, which may be context-dependent, but are in factconnected to universal human rights, such as the right to privacy, freedom fromdiscrimination, and freedom of expression. We highlight the need to examine thebroad social impacts of this technology, and to bring ethical and human rightsconsiderations to every stage of the application life-cycle, from taskformulation and dataset design, to model training and evaluation, toapplication deployment. Guided by these principles, we identify severalopportunities for rights-respecting, socio-technical solutions to detect andconfront online abuse, including `nudging', `quarantining', value sensitivedesign, counter-narratives, style transfer, and AI-driven public educationapplications.

Confronting Abusive Language Online: A Survey from the Ethical and Human Rights Perspective