We can build a system to identify "people in society who truly need help"
by breaking the problem into several layers: data collection, assessment indicators, privacy and ethics, and resource matching. The outline is as follows:
Data Collection Layer
This is the foundation for the system to "know who might need help."
- Public Data: Government statistics, social assistance records, healthcare data (anonymized), educational resources data, etc.
- Community Data: Requests for help on social media, online forums, and community group posts.
- Automated Reporting/Application System: Allows individuals to proactively report their difficulties, enabling the system to perform initial categorization.
- IoT and Environmental Sensors (optional): Such as smart city monitoring and disaster monitoring, which can identify populations in urgent need.
Assessment Indicators Layer
AI requires clear indicators or features to determine the "degree of need for help":
- Economic Hardship: Income below local average, unemployment, high rent/mortgage pressure.
- Health Hardship: Chronic illnesses, mental health issues, urgent medical needs.
- Social Isolation: Elderly living alone, lack of family support, weak community connections.
- Danger/Emergency Situations: Domestic violence, natural disasters, sudden accidents, homelessness.
These indicators can be quantified using a multidimensional scoring system to measure the "urgency of need."
Privacy and Ethics Layer
- Anonymization and Data Security: Ensure that collected information is not misused.
- Voluntary Participation: Give people the option, especially for sensitive data.
- Avoiding Bias: AI training data must be diverse to avoid focusing only on certain groups or communities.
- Transparency: Allow the public to understand how the system determines who needs help.
Resource Matching Layer
- Intelligent Recommendation: Automatically match available resources (financial aid, medical care, psychological counseling, volunteer services) to those in need.
- Priority Sorting: AI can determine who should receive help first based on urgency, self-help ability, and resource constraints.
- Tracking and Feedback: Ensure that help is effectively delivered and continuously monitor for improvement.
System Design Illustration
It can be visualized as a cyclical process:
Data Collection → AI Risk Assessment → Priority Sorting for Assistance → Resource Recommendation and Allocation → Outcome Tracking → Model Adjustment
Such a system can both "automatically identify" and "accurately allocate resources."