Data analytics and machine learning for gender-based violence prevention: A framework for policy design and intervention strategies

Abstract

Gender-based violence (GBV) remains a significant global issue, and the application of data analytics and machine learning (ML) presents new opportunities for more effective prevention and intervention strategies. This paper explores the role of data analytics and ML in addressing GBV, focusing on their application in trend analysis, risk factor prediction, and hotspot mapping. The review examines existing tools and methods used to analyze patterns of GBV, predict high-risk situations, and identify areas with elevated GBV incidence. These technologies can provide valuable insights for stakeholders in law enforcement, social services, and policymaking. The paper proposes a conceptual model for enhancing GBV prevention efforts by integrating predictive algorithms with socio-cultural data. This model aims to create data-driven frameworks for policy design, helping to identify emerging risks, design targeted intervention programs, and assess the effectiveness of prevention initiatives. By combining quantitative data with qualitative insights from community surveys, the model facilitates a more holistic approach to tackling GBV. Furthermore, the study addresses the ethical implications of using data and ML in GBV prevention. Issues such as privacy, data security, and bias in algorithmic decision-making are explored, emphasizing the need for ethical guidelines and transparency in the use of these technologies. The importance of community engagement in data collection, program design, and evaluation is also highlighted. Engaging communities ensures that interventions are culturally sensitive, locally relevant, and more likely to succeed in reducing GBV. In conclusion, data analytics and ML offer promising tools for transforming GBV prevention, but their effective implementation requires careful attention to ethical considerations and active involvement of affected communities. This paper provides a framework for utilizing these technologies to inform policy decisions and create more impactful, evidence-based interventions for GBV prevention.

Keywords: Gender-Based Violence, Data Analytics, Machine Learning, Risk Prediction, Hotspot Mapping, Intervention Strategies, Policy Design, Ethical Implications, Community Engagement.