Empowering Safe Online Spaces: AI in Gender Violence Detection and Prevention

Authors

  • Rahul Manche
  • FNU Samaah
  • Tejaswini Bollikonda
  • Praveen Kumar Myakala

DOI:

https://doi.org/10.46243/jst.2025.v10.i02.pp39-50

Keywords:

Gender-based online violence (GBOV),, Artificial intelligence (AI), Hate speech detection, Content moderation, Algorithmic bias

Abstract

Gender-based online violence (GBOV) is a pervasive issue that disproportionately impacts women and marginalized genders, leading to psychological distress, economic consequences, and restricted participation in digital spaces. This article examines how Artificial Intelligence (AI) offers innovative solutions to detect and mitigate GBOV through tools such as sentiment analysis, hate speech detection, image recognition, and behavioral analysis. AI-powered interventions have significantly enhanced the ability to identify harmful content, automate moderation, and empower victims by providing real-time safeguards. Despite these advancements, challenges such as algorithmic bias, privacy concerns, and the evolving tactics of online abuse remain critical obstacles. This study highlights the importance of developing ethical AI systems, fostering multi-stakeholder collaboration, and implementing robust regulatory frameworks to create safer, more equitable online environments for all.

References

European Commission. 2021. Proposal for a Regulation Laying Down Harmonized Rules on Artificial Intelligence (AI Act). https:// eur-lex.europa.eu/legal-content/ EN/ TXT/?uri=CELEX:52021PC0206

United Nations Broadband Commission. 2021. Towards a Resilient, Safe, and Inclusive Digital Future. https://broadbandcommission.org

Australian eSafety Commissioner. 2021. Australia’s eSafety Act: Enhancing Online Safety. https://www.esafety.gov.au

Nicola Henry, Asher Flynn, and Anastasia Powell. 2020. Technology-facilitated domestic and sexual violence: A review. Violence against women 26, 15-16 (2020), 1828–1854. https:// doi.org/10.1177/1077801219875821

StopNCII.org Initiative. 2021. StopNCII.org: An AI-driven solution for combating image-based abuse. https://stopncii.org

Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM computing surveys (CSUR), 54(6), 1-35. https://doi. org/10.1145/3457607

The Economist Newspaper. 2021. Measuring the prevalence of online violence against women. https://onlineviolencewomen.eiu.com

WITNESS Organization. 2022. WITNESS: Addressing Online Harms through AI and Collaboration. https://witness.org

World Health Organization. 2021. Violence Against Women Prevalence Estimates. https:// www.who.int/publications/i/item/violenceagainst-women

Meta AI Research. 2022. Our New AI System to Help Tackle Harmful Content. https://about. fb.com/news/2021/12/metas-new-ai-systemtackles-harmfulcontent

Sarah T. Roberts. 2019. Behind the Screen: Content Moderation in the Shadows of Social Media. Yale University Press. https://doi. org/10.2307/j.ctvhrcz0v

Sayar Ghosh Roy, Ujwal Narayan, Tathagata Raha, Zubair Abid, and Vasudeva Varma. 2021. Leveraging multilingual transformers for hate speech detection. arXiv preprint arXiv:2101.03207 (2021). https://doi. org/10.48550/arXiv.2101.03207

Raiswa Saha, Sakshi Ahlawat, Umair Akram, Uttara Jangbahadur, Amol S Dhaigude, Pooja Sharma, and Sarika Kumar. 2024. Online abuse: a systematic literature review and future research agenda. International Journal of Conflict Management (2024). https://doi. org/10.1108/IJCMA-09-2023-0188

Maarten Sap et al. 2020. The Risk of Racial Bias in Hate Speech Detection. Proceedings of the ACL (2020),1668–1679. https://doi.org/10.18653/ v1/P19-1163

Anna Schmidt and Michael Wiegand. 2017. A survey on hate speech detection using natural language processing. ACL Anthology (2017), 1–10. https://doi.org/ 10.18653/v1/W17-1101

UNESCO. 2022. Media and Information Literacy: UNESCO’s Framework for Digital Literacy. https://www.unesco.org/en/mediainformation-literacy

UNESCO. 2022. UNESCO Guidelines for Ethical AI Development. https://unesdoc.unesco.org/ ai-guidelines

Myakala, P. K., Jonnalagadda, A. K., & Bura, C. (2025). The Human Factor in Explainable AI Frameworks for User Trust and Cognitive Alignment. https://dx.doi.org/10.2139/ ssrn.5103067

Myakala, P. K. (2024). Consciousness in Machines: A Critical Exploration. International Journal of Multidisciplinary Research and Analysis, 7(12), 10-47191. https://doi. org/10.47191/ijmra/v7-i12-18

Published

2025-02-20

How to Cite

Rahul Manche, FNU Samaah, Tejaswini Bollikonda, & Praveen Kumar Myakala. (2025). Empowering Safe Online Spaces: AI in Gender Violence Detection and Prevention. Journal of Science & Technology , 10(2), 39–50. https://doi.org/10.46243/jst.2025.v10.i02.pp39-50