Online Abuse and Human Rights: WOAH Satellite Session at RightsCon 2020
By Vinodkumar Prabhakaran, Zeerak Talat, Seyi Akiwowo, Bertie Vidgen. 2020
11 citations recorded by the source corpus.
In 2020 The Workshop on Online Abuse and Harms (WOAH) held a satellite panel at RightsCons 2020, an international human rights conference. Our aim was to bridge the gap between human rights scholarship and Natural Language Processing (NLP) research communities in tackling online abuse. We report on the discussions that took place, and present an analysis of four key issues which emerged: Problems in tackling online abuse, Solutions, Meta concerns and the Ecosystem of content moderation and research. We argue there is a pressing need for NLP research communities to engage with human rights perspectives, and identify four key ways in which NLP research into online abuse could immediately be enhanced to create better and more ethical solutions.
Subfields: Artificial Intelligence, Communication, Safety, Robustness & Interpretability
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Vinodkumar Prabhakaran
- Aida Mostafazadeh Davani
- Mark Díaz
- Ben Hutchinson
- Su Lin Blodgett
- Irene Solaiman
- Dylan Baker
- Alexandra Sasha Luccioni
In Communication
- Vinodkumar Prabhakaran
- Mark Díaz
- Aida Mostafazadeh Davani
- Alexandra Sasha Luccioni
- Dylan Baker
- Su Lin Blodgett
- Irene Solaiman
- Zeerak Talat
In Safety, Robustness & Interpretability
- Vinodkumar Prabhakaran
- Ben Hutchinson
- Su Lin Blodgett
- Dylan Baker
- Mark Díaz
- Aida Mostafazadeh Davani
- Irene Solaiman
- Zeerak Talat
Citations in the corpus
- Evaluating the Social Impact of Generative AI Systems in Systems and Society (2023, background): “Taxonomies of risks and harms of generative AI systems [80], including their impacts on human rights [111, 186], strongly overlap with what should be evaluated.”
- Cultural Incongruencies in Artificial Intelligence (2022, background): “For instance, content moderation systems meant to detect offensiveness and misinformation may miss culture-specific offensive terms and interpretations allowing toxic or violent speech to propagate for some cultural settings [21, 14].”
- Dealing with Disagreements: Looking Beyond the Majority Vote in Subjective Annotations (2021, background): “While whether a piece of text is hateful or not depends also on the context (Prabhakaran et al., 2020), not much work investigated the human annotator biases present in the training labels, and how they impact downstream predictions.”