Explainable AI for Real-Time Social & Environmental Data to Drive Sustainable Development.

This project aims to develop and deploy explainable AI solutions for analyzing real-time social and environmental datasets. By leveraging advanced machine learning techniques, streaming data analytics, and interpretable models, the project seeks to provide actionable insights that inform policies and interventions for sustainable development goals (SDGs). Key objectives include:

1. Real-Time Analytics: Implementing pipelines for continuous collection, processing, and visualization of social and environmental data.

2. Explainable AI Models: Developing interpretable AI techniques to ensure transparency, fairness, and trust in predictions.

3. Scalable Solutions: Ensuring AI models can handle large-scale datasets and diverse data types from multiple sources.

4. Policy & Decision Support: Providing stakeholders with actionable insights for interventions in areas such as public health, environmental conservation, and social welfare.

We are seeking collaboration with academic institutions, research labs, NGOs, and technology partners interested in AI for social impact, data integration, and sustainable development initiatives.