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Cyberbullying Detection with Fairness Constraints

Cyberbullying is a widespread adverse phenomenon among online social interactions in today's digital society. While numerous computational studies focus on enhancing the cyberbullying detection performance of machine learning algorithms, proposed models tend to carry and reinforce unintended social biases. In this study, we try to answer the research question of "Can we mitigate the unintended bias of cyberbullying detection models by guiding the model training with fairness constraints?".


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2021 ACM Conference on Fairness, Accountability, and Transparency

The 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT, formerly known as FAT*) is an interdisciplinary conference currently soliciting work from a wide variety of disciplines, including computer science, statistics, law, social sciences, the humanities, and education. Submissions around the topics of fairness, accountability and transparency to be submitted until October 7th, 2020.