Module Assessment

1.

Which best illustrates how AI is currently supporting nonprofit work across different sectors?

Automating all field operations so humanitarian teams no longer need to assess needs in person.

Using satellite imagery, chatbots, and environmental data analysis to improve decision-making and deliver faster, more targeted support.

Replacing development practitioners by allowing AI models to independently design community programs.

Centralizing all nonprofit operations into a single AI platform to eliminate staffing and coordination challenges.

2.

Which most accurately reflects accountability in the use of AI within nonprofit or humanitarian settings?

Relying on vendor documentation to assume that an AI system meets ethical and technical standards.

Ensuring that AI-generated outputs can be reviewed, challenged, and traced to clear human oversight structures.

Allowing AI systems to operate autonomously in low-risk decisions to reduce staff workload.

Protecting stakeholder data by limiting access to AI tools, even if model decisions remain opaque.

3.

Across the case studies presented, what common pattern best describes how nonprofits are using AI to strengthen program impact?

AI systems are primarily replacing manual field activities to reduce staffing needs in low-resource settings.

AI tools are being used to generate automated decisions at scale, limiting the need for human review in operational workflows.

AI enhances real-time situational awareness and decision-making through geospatial analysis, predictive modelling, and data-driven insights while keeping human experts in control of final actions.

AI applications are focused mainly on donor engagement improvements, with limited value for core program delivery or field operations.

Feedback