Talk · International Conference on Information Management and Engineering · September 24, 2025

Ethical Research on Fairness and Privacy in Kazakh Toxicity Detection

Altanbek Zulkhazhav, Mamyr Altaibek, Assel Omarbekova, Sultan Beibarys, Orazali Begenov

Abstract

The explosive growth of social media has intensified the need for reliable toxicity-detection systems that keep online spaces safe and inclusive. Yet low-resource languages such as Kazakh remain under-served because limited data, agglutinative morphology and cultural nuance exacerbate algorithmic bias and privacy leakage. We introduce a fairnessand privacy-aware framework that combines named-entityrecognition (NER) anonymisation, adversarial debiasing and multi-task learning. First, the $\mathbf{1 0 0}$ k-comment Jigsaw Toxicity corpus is machine-translated into Kazakh and partially backtranslated for quality control. A multilingual DistilBERT encoder processes tokenised text, while an NER module masks PERSON, LOCATION and ORGANISATION entities to protect users. A gradient-reversal branch discourages the encoder from encoding sensitive attributes, and a lightweight reject-option layer adjusts borderline decisions in favour of under-represented groups. Comprehensive evaluation on the translated test set shows the proposed model reaches 92.45% accuracy, 75.90% precision, 72.50% recall and 74.15% F1-score. At the same time it lowers Equal-Opportunity Difference by 26%, detects 80% of sensitive entities and preserves 85% anonymisation consistency. The results demonstrate that strong predictive performance, fairness and privacy can be delivered simultaneously in a lowresource setting, offering a practical path toward ethical naturallanguage processing for the Kazakh digital ecosystem.

Hate Speech and Cyberbullying DetectionEthics and Social Impacts of AITopic Modelingtoxicity detectionKazakh languagealgorithmic fairness

Ethical Research on Fairness and Privacy in Kazakh Toxicity Detection

Abstract

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