Artificial intelligence is rapidly becoming a core infrastructure for modern organizations. For NGOs and nonprofit organizations (NPOs), however, adopting AI is not simply a technical decision: it is an ethical, political, and strategic one. These organizations frequently handle sensitive beneficiary data, donor information, policy advocacy, and human rights documentation, making their technology choices uniquely consequential.

The question is not merely “Which AI is most powerful?” but rather:

• Which AI is transparent and accountable
• Which protects sensitive communities and data
• Which is sustainable for long-term civil society use

This paper outlines the current landscape of AI tools and proposes a framework NGOs can use to evaluate them responsibly.

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1. Why AI Decisions Are Different for NGOs

Most nonprofits operate under three constraints:

1. Ethical accountability to communities and donors
2. Legal compliance (GDPR, data protection, humanitarian standards)
3. Resource limitations (free or low-cost tools are often necessary)

At the same time, generative AI tools rely heavily on massive training datasets, opaque algorithms, and centralized infrastructure, raising questions about transparency, bias, and data governance.

Academic research consistently identifies privacy, bias, and misuse risks in large language models such as ChatGPT. (arXiv)

For organizations advocating for public trust, adopting opaque AI systems can create contradictions between mission and infrastructure.

2. The Transparency Problem in Commercial AI

Most widely used AI systems today are closed models developed by large technology companies. Their internal training data, model weights, and decision logic are not publicly accessible.

This creates several challenges:

1. Training Data Uncertainty

AI models like ChatGPT are trained on large amounts of publicly available internet data. (OpenAI Help Center)

However, critics argue this often includes copyrighted or personal information, leading to legal disputes and regulatory scrutiny. For example, courts in Europe have already ruled that certain training uses violated copyright law. (The Guardian)

2. Data Governance Risks

Using free AI tools can expose sensitive information. Some privacy analyses warn that user conversations may be used to improve models unless settings are changed or enterprise plans are used. (Terms.Law)

For nonprofits handling donor lists, refugee data, or medical information, this is a significant compliance risk.

3. Legal and Political Pressure

AI companies are increasingly entangled in regulatory and legal disputes. In one case, OpenAI was ordered by a court to retain deleted conversations as evidence in litigation, affecting many users. (The Verge)

This illustrates a key reality: organizations using centralized AI services do not control their data environment.

3. The Political Dimension of AI

Civil society organizations must also consider the political implications of AI infrastructure.

Large AI models are currently dominated by a handful of companies in the United States and China. This concentration creates:

• geopolitical dependencies
• regulatory risks
• ideological influence through algorithmic bias

Research has shown that conversational AI systems can exhibit measurable political bias depending on training data and model design. (arXiv)

For NGOs working in governance, democracy, or advocacy, relying on a single corporate AI platform may undermine neutrality or independence.

4. Why Open or Privacy-Focused AI Is Emerging

In response to these concerns, several open and privacy-focused AI projects have emerged.

Examples include:

• European privacy-focused assistants
• nonprofit research models
• open-weight large language models

These initiatives aim to provide verifiable transparency and sovereignty over AI systems.

For example, Switzerland released an open model called Apertus with publicly available source code and training data, explicitly designed to meet regulatory transparency requirements. (The Verge)

Similarly, nonprofit research groups like EleutherAI develop open models and datasets to reduce dependence on proprietary AI systems. (Wikipedia)

5. The Case Against Blindly Using ChatGPT

This does not mean tools like ChatGPT should never be used by nonprofits. They can be extremely valuable for:

• drafting reports
• summarizing documents
• grant proposal assistance
• translation and communications

However, NGOs should understand the limitations.

Key concerns

1. Data privacy
Sensitive data entered into free tools may be stored or processed externally.

2. Limited transparency
Closed models cannot be independently audited.

3. Corporate dependency
Nonprofits may become reliant on tools whose pricing or policies change.

4. Legal exposure
Organizations remain responsible for incorrect or biased AI output.

Researchers warn that these tools are statistical pattern systems rather than knowledge engines, meaning errors and hallucinations remain unavoidable. (arXiv)

6. AI Services Overview for NGOs

Below is a simplified comparison of several current AI systems relevant to nonprofits.

Interpretation

• Highest transparency: open-source models
• Best privacy: self-hosted or encrypted services
• Most powerful but risky: closed commercial models

7. Recommended AI Strategy for NGOs

Instead of choosing a single AI platform, nonprofits should adopt a layered AI strategy.

1. Public tools for low-risk tasks

Use commercial AI for:

• writing assistance
• brainstorming
• public information analysis

2. Privacy-focused AI for internal work

Use privacy-oriented systems (e.g., encrypted assistants) for:

• internal reports
• donor communications
• organizational planning

3. Self-hosted open models for sensitive programs

For organizations working with vulnerable populations, consider:

• open models
• on-premise AI
• restricted training data

This approach maximizes productivity while maintaining ethical responsibility.

8. Governance Recommendations

Every nonprofit adopting AI should establish an internal AI policy covering:

1. Acceptable data types for AI input
2. Required human review of AI outputs
3. Transparency about AI use in public materials
4. Vendor risk assessments
5. Documentation of AI decision-making processes

Many experts emphasize that governance matters more than the specific AI model used.

Conclusion

Artificial intelligence will inevitably become part of nonprofit infrastructure. The key challenge for NGOs is not simply whether to adopt AI—but how to do so responsibly.

Closed corporate AI tools provide convenience and power, but they also introduce risks related to privacy, governance, and political dependency.

For mission-driven organizations, the most sustainable path forward combines:

• open technologies
• privacy-focused tools
• clear governance frameworks

The future of ethical AI in civil society will likely depend on a hybrid ecosystem where nonprofits can benefit from AI innovation without sacrificing transparency or autonomy.

References

• Data Orchard – AI in Nonprofits
  https://www.dataorchard.org.uk/resources/ai-in-nonprofits
• AI for Humanity Report (2025)
  https://benrmatthews.com/how-nonprofits-use-ai-the-2025-ai-for-humanity-report/
• GDPR Guidance for Charities Using AI
  https://www.hinchilla.com/learn/gdpr-for-charities-chatgpt-ai-guide
• Running AI Locally for Privacy-Conscious Nonprofits
  https://onehundrednights.com/article/local-ai-privacy/
• ChatGPT Risks for Nonprofits
  https://levacloud.com/2025/09/04/chatgpt-for-nonprofits/
• Lumo Privacy AI Assistant
  https://en.wikipedia.org/wiki/Lumo_(AI_assistant)
• Security and Ethical Risks in ChatGPT
  https://arxiv.org/abs/2305.08005