Cyberbullying Detection With Fairness Constraints

By Oguzhan Gencoglu. 2020

58 citations recorded by the source corpus.

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.

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

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Citations in the corpus

Language (Technology) is Power: A Critical Survey of “Bias” in NLP (2020, neutral): “…Ruane et al. (2019); Sap et al. (2019); Sheng et al. (2019); Sun et al. (2019); Sweeney and Najafian (2019); Vaidya et al. (2019); Gaut et al. (2020); Gencoglu (2020); Hovy et al. (2020); Hutchinson et al. (2020); Kim et al. (2020); Peng et al. (2020); Rios (2020); Sap et al. (2020); Shah et al.…”
Anticipating Safety Issues in E2E Conversational AI: Framework and Tooling (2021, background): “Gencoglu (2020) propose adding fairness constraints to a cyberbullying detection system.”
Recipes for Safety in Open-domain Chatbots (2020, methodology): “Waseem et al. (2017) advocate for partitioning abusive content according to what entity it is directed to, an approach adopted by the OLID/OffensEval datasets (Zampieri et al., 2019, 2020).”
Guiding the Release of Safer E2E Conversational AI through Value Sensitive Design (2022, background): “Possible mitigations include using race and dialect priming (Sap et al., 2019), using adversarial training techniques (Xia et al., 2020), adding fairness constraints (Gencoglu, 2020), or relabeling data used during training (Zhou et al., 2021).”
Confronting Abusive Language Online: A Survey from the Ethical and Human Rights Perspective (2020, methodology): “Gencoglu (2020) also used error disparity across groups as a measure of fairness and defined fairness constraints to guide the training of a neural model.”
Is Your Toxicity My Toxicity? Exploring the Impact of Rater Identity on Toxicity Annotation (2022, background): “(7) As focusing on model-robustness-evaluation to detect identity markers like race and gender correctly [36], multi-lingual hate speech classification [5], and model-fairness-evaluation [17, 19].”
Quantifying Social Biases in NLP: A Generalization and Empirical Comparison of Extrinsic Fairness Metrics (2021, background): “This is all the more important for FPED and FNED, as they have been very inﬂuential, with many works relying exclusively on these metrics (Rios, 2020; Huang et al., 2020b; Gencoglu, 2021; Rios and Lwowski, 2020).”