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Title: AI-Enhanced Early Warning System for Gender based violence

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Title: AI-Enhanced Early Warning System for Gender based violence

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Hauwa'u Umar May 5, 2025
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Challenge: AI FOR PEACE CHALLENGE

Industries

Health and wellness

Technologies

Narrow AINeuro-symbolic AI

Tags

DF rules

Description

The description :

"Aggregate GBV data from social media, helplines, healthcare & police reports. Train ML/NLP models to detect abuse escalation patterns. Apply geospatial analytics to identify hotspots. Deliver real-time alerts to NGOs, law enforcement & responders via dashboard/app. Secure, anonymous reporting with AI triage prioritizes urgent cases, ensuring privacy via federated learning & encryption."

Detailed Idea

Alignment with DF goals (BGI, Platform growth, community)

Alignment:

 

 

This initiative aligns with DeepFunding’s vision by deploying AI to protect human rights and safety. It leverages SingularityNET for open collaboration with NGOs and experts, allowing shared improvement of predictive models. By prioritizing privacy and ethical data use, it adheres to Beneficial AGI principles of serving humanity. Focusing on vulnerable communities and proactive intervention, it embodies SingularityNET’s commitment to AI for social good. The open, decentralized design and emphasis on community input reflect DeepFunding’s values of inclusive, beneficial AI development.

Problem description

Problem statement:

GBV is a global crisis often unreported until it escalates. Fragmented data across NGOs, healthcare, and law enforcement hinders early trend detection. Communities lack predictive tools to identify escalating risks, leading to late interventions. This reactive response allows GBV to persist, endangering vulnerable groups."

Proposed Solutions

The solution:

This system detects GBV risk signals by aggregating helpline logs, social media, and healthcare records. AI identifies patterns indicating rising abuse, generating geospatial alerts for NGOs and authorities. A secure, anonymous reporting tool lets community members submit tips, prioritized by AI urgency. Collaborative predictive models improve accuracy while safeguarding privacy, enabling proactive intervention."

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