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

By Svetlana Kiritchenko, Isar Nejadgholi, Kathleen C. Fraser. 2020

116 citations recorded by the source corpus.

The pervasiveness of abusive content on the internet can lead to severe psychological and physical harm. Significant effort in Natural Language Processing (NLP) research has been devoted to addressing this problem through abusive content detection and related sub-areas, such as the detection of hate speech, toxicity, cyberbullying, etc. Although current technologies achieve high classification performance in research studies, it has been observed that the real-life application of this technology can cause unintended harms, such as the silencing of under-represented groups. We review a large body of NLP research 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 and non-discrimination, human control of technology, professional responsibility, and promotion of human values. In many cases, these principles relate not only to situational ethical codes, which may be context-dependent, but are in fact connected to universal human rights, such as the right to privacy, freedom from discrimination, and freedom of expression. We highlight the need to examine the broad social impacts of this technology, and to bring ethical and human rights considerations to every stage of the application life-cycle, from task formulation and dataset design, to model training and evaluation, to application deployment. Guided by these principles, we identify several opportunities for rights-respecting, socio-technical solutions to detect and confront online abuse, including ‘nudging’, ‘quarantining’, value sensitive design, counter-narratives, style transfer, and AI-driven public education applications.evaluation, to application deployment. Guided by these principles, we identify several opportunities for rights-respecting, socio-technical solutions to detect and confront online abuse, including 'nudging', 'quarantining', value sensitive design, counter-narratives, style transfer, and AI-driven public education applications.

Subfields: Artificial Intelligence, Social Psychology, Natural Language Processing, Safety, Robustness & Interpretability

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Citations in the corpus

Societal Biases in Language Generation: Progress and Challenges (2021, background): “Task formulation and application deployment are also part of NLG task pipelines (Kiritchenko et al., 2020), though we do not focus on biases in these areas.”
Undesirable biases in NLP: Averting a crisis of measurement (2022, background): “…and stakeholders (e.g., designers, owners, and users of these NLP systems, and those potentially harmed by its implementation) in the measurement tool design process (see also Blodgett et al., 2020; Bender et al., 2021; Kiritchenko et al., 2021; Talat et al., 2022; Dev et al., 2022, i.a.).”
Your fairness may vary: Pretrained language model fairness in toxic text classification (2021, background): “Due to an increase in online harassment (Vogels, 2021) and the potential of both propagating harmful stereotypes of minority groups and/or inadvertently reducing their voices, the task of predicting toxicity in text has received increased attention in recent years (Kiritchenko et al., 2021).”
Human-Machine Collaboration Approaches to Build a Dialogue Dataset for Hate Speech Countering (2022, background): “Other reviews include the analysis of ethical implications (Kiritchenko et al., 2021) and of problems such as bias replication (Binns et al., 2017; Davidson et al., 2019; Vidgen and Der-czynski, 2020; Sap et al., 2019; Tsvetkov, 2020).”