AI in NGOs

Introduction

Artificial Intelligence (AI) is increasingly being integrated into various sectors, including non-governmental organizations (NGOs). This integration aims to enhance efficiency, accuracy, and decision-making in NGO operations. The following synthesis presents key insights from recent research on the role of AI in NGOs, focusing on its benefits, challenges, and ethical considerations.

Key Insights

Enhancement of NGO Operations:
- AI is being used to improve program design, resource management, monitoring and evaluation, and predictive analysis in NGOs .
- AI techniques help with prediction, data analysis, modeling, and decision-making, addressing non-standard conditions like missing data and multiple competing objectives.
Ethical Considerations:
- NGO documents on AI ethics cover a broader range of topics and are more engaged with law and regulation compared to private sector documents.
- Ethical concerns in AI use include ensuring that social good is prioritized and that AI applications are developed through participatory processes .
Collaborative Efforts:
- Collaborations between AI researchers and NGOs are beneficial, as they combine technical expertise with domain knowledge and networks in developing countries.
- Workshops and hackathons are effective in fostering understanding and collaboration between AI researchers and NGO stakeholders.

Conclusion

AI is revolutionizing NGO operations by enhancing various aspects such as program design and resource management. Ethical considerations are paramount, with NGOs emphasizing broader and more participatory approaches to AI ethics. Collaborative efforts between AI researchers and NGOs are crucial for addressing unique challenges and ensuring that AI applications serve the social good effectively.

Summary

Introduction

Key Insights

Conclusion

Sources and full results

The Role of Artificial Intelligence in Revolutionizing NGOs' Work

AI can revolutionize NGOs' work by enhancing efficiency, accuracy, and decision-making, but ethical considerations and challenges must be addressed.

AI Ethics in the Public, Private, and NGO Sectors: A Review of a Global Document Collection

AI ethics documents from public and NGOs sectors show more ethical breadth, engage with law and regulation, and are more participatory than private entities.

AI for the Social Good (Dagstuhl Seminar 19082)

AI and machine learning researchers can benefit NGOs by collaborating on social good goals, identifying key challenges, and defining guidelines for future AI for Social Good initiatives.

Drivers, barriers and social considerations for AI adoption in business and management: A tertiary study

AI adoption in business and management is driven by economic factors, but barriers include technical and social considerations, requiring more rigorous evaluation and hybrid solutions.

Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy

AI has the potential to revolutionize industries and society, but challenges and research agendas must be addressed to ensure its successful implementation and future impact.

Principles alone cannot guarantee ethical AI

Principles alone cannot guarantee ethical AI, as AI development lacks common aims, fiduciary duties, professional history, and accountability mechanisms compared to medicine.

Artificial Intelligence in Medicine

AI in medicine has two main branches: virtual and physical, with potential applications in informatics, robotics, and drug delivery.

How AI can be a force for good

An ethical framework can help harness AI's potential while maintaining human control, ensuring human well-being and promoting ethical regulations for AI regulation.

Harnessing artificial intelligence (AI) to increase wellbeing for all: The case for a new technology diplomacy

Technology diplomacy can facilitate global alignment of AI policy and governance, enhancing human welfare and preventing malicious uses while addressing diverse national conditions.

The global landscape of AI ethics guidelines

Global AI ethics guidelines convergence on five core principles, but differ on how they are interpreted and implemented.

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