  To support safety and inclusion in online communications, significant efforts
in NLP research have been put towards addressing the problem of abusive content
detection, commonly defined as a supervised classification task. The research
effort has spread out across several closely related sub-areas, such as
detection of hate speech, toxicity, cyberbullying, etc. There is a pressing
need to consolidate the field under a common framework for task formulation,
dataset design and performance evaluation. Further, despite current
technologies achieving high classification accuracies, several ethical issues
have been revealed. We bring ethical issues to forefront and propose a unified
framework as a two-step process. First, online content is categorized around
personal and identity-related subject matters. Second, severity of abuse is
identified through comparative annotation within each category. The novel
framework is guided by the Ethics by Design principle and is a step towards
building more accurate and trusted models.
