Cyberbullying Detection With Fairness Constraints
Cyberbullying Detection With Fairness Constraints
复制标题
具有公平性约束的网络欺凌检测
DOI：
10.1109/mic.2020.3032461
复制
发表时间：
2020
影响因子：
3.2
通讯作者：
O. Gencoglu
中科院分区：
文献类型：
--
作者：
O. Gencoglu
网络欺凌是当今数字社会在线社交互动中普遍存在的不良现象。虽然许多计算研究侧重于提高机器学习算法的网络欺凌检测性能，但所提出的模型往往带有并强化了意想不到的社会偏见。在本研究中，我们试图回答“我们能否通过公平约束指导模型训练来减轻网络欺凌检测模型的意外偏见”这一研究问题。为此，我们提出了一种模型训练方案，该方案可以使用公平性约束，并在不同的数据集上验证我们的方法。我们证明了各种类型的意外偏差可以在不损害模型质量的情况下成功地减轻。我们相信，我们的工作有助于为网络社会健康寻求公正、透明和合乎道德的机器学习解决方案。
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.
DOI：
10.1073/pnas.1720347115
发表时间：
2018-04-17
影响因子：
11.1
作者：
Garg, Nikhil;Schiebinger, Londa;Zou, James
通讯作者：
Zou, James