Cyberbullying Detection with Fairness Constraints
This addresses fairness issues in cyberbullying detection for online social platforms, representing an incremental improvement over existing methods.
The study tackled the problem of unintended social biases in cyberbullying detection models by proposing a training scheme with fairness constraints, demonstrating that various biases can be mitigated without impairing model quality.
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?". For this purpose, we propose a model training scheme that can employ fairness constraints and validate our approach with different datasets. We demonstrate that various types of unintended biases can be successfully mitigated without impairing the model quality. We believe our work contributes to the pursuit of unbiased, transparent, and ethical machine learning solutions for cyber-social health.