Limits of AI accuracy and reliability

  • 7 minutes

While AI tools can generate content quickly and efficiently, they make mistakes. AI models can produce outputs that contain factual errors, outdated information, or fabricated claims. This occurs because AI systems generate responses based on statistical patterns learned from training data rather than accessing real-time, verified, or authoritative sources. Inaccurate outputs can lead to liability, reputation loss, or harm.

For nonprofits working in sensitive areas such as humanitarian response, policy advocacy, legal aid, or community health, inaccurate AI-generated content can have serious consequences. These risks range from unintentionally spreading misinformation that harms vulnerable populations to undermining credibility with donors, partners, and the communities served. Even small inaccuracies can distort findings, misinform decisions, or breach program and safeguarding standards.

These are some factors affecting the reliability of AI outputs:

  • The clarity and specificity of the prompt
  • The quality and recency of the data on which the model was trained
  • The cultural and contextual nuances that aren't captured in global datasets
  • The complexity or sensitivity of the task being performed

Some common forms of inaccurate outputs include fake news, misinformation, personalized impersonation, manipulation, and false attributions. AI models can generate fake content, known as "deepfakes," such as pictures, videos, and voice simulations that can be used to spread false information and manipulate audiences on a large scale.

How nonprofits address these issues

Nonprofit teams must treat AI-generated content as a starting point, not as an authoritative source. Any AI-produced text, analysis, or recommendation intended for decision-making, public communication, or donor reporting requires careful human review and verification. Nonprofits can reduce risks and improve output quality by adopting a combination of the following technical, organizational, and human-centered strategies.

Maintain human oversight (human-in-the-loop)

Ensure that teams and experts review AI outputs for:

  • Accuracy: Are facts correct? Are sources credible?
  • Context: Does the content fit the local, cultural, and operational reality?
  • Local relevance: Does it reflect community needs, local language norms, and up-to-date field conditions?

Use retrieval-augmented generation (RAG)

RAG reduces errors and ensures outputs align with organizational knowledge. RAG improves accuracy by grounding the AI's responses in your own verified documents, such as:

  • Program manuals
  • Safeguarding protocols
  • Donor requirements
  • Research reports
  • Local context briefings

Fine-tuning or custom models

Fine-tune AI models using your organization's domain-specific data or examples, so outputs reflect:

  • Accurate terminology
  • Reliable program information
  • Locally relevant content
  • Sector-specific standards

Prompt engineering

Prompts should clearly state the goal, provide relevant context, specify the source of information, and set expectations for tone, format, or length. This reduces ambiguity and ensures accurate, reliable responses:

  • Use only the information provided in the uploaded documents.
  • If unsure, state uncertainty rather than guessing.
  • List the limitations of your answer.

For a quick prompt formula include:

  • Goal: What do you want the AI to do?
  • Context: Why or what background information matters?
  • Expectations: Tone, format, length, style?
  • Source: Which data or documents should it use?

Workflow boundaries

Define exactly what the AI can and can't do. This ensures high-risk tasks remain under human control. For example:

  • AI may draft summaries but not send emails
  • AI may generate options but not finalize recommendations
  • AI may produce outlines but not publish reports

Understand legislation and regulations

Transparency and fair use of AI: Transparency in AI means being open about how AI systems work, what data they use, and how decisions are made. This helps users understand outcomes, identify potential biases, and build trust in AI-generated content.

Accountability and human oversight: Organizations using AI are responsible for ensuring that AI systems follow the do-no-harm principle. Human judgment must remain central, with clear responsibility for how AI tools are designed, deployed, and monitored, especially if harm occurs.

Explainability for non-experts: AI systems should be understandable to people without technical backgrounds. Clear explanations and documentation help users know how outputs are generated, supporting informed use, long-term sustainability, and wider stakeholder engagement.

Value maintenance and sustainability: Beyond innovation, AI systems require ongoing care, maintenance, and updates. Investing in long-term support, such as open-source and public-good approaches, helps ensure AI continues to serve diverse groups fairly over time.

Government regulations and AI legislation

Governments and regulatory bodies worldwide are developing rules to ensure that AI-generated content is accurate, transparent, and responsibly used, especially when the content influences public welfare.

Overall, AI regulation varies widely among governments, creating challenges for organizations operating across borders. Laws also struggle to keep pace with rapid technological change, requiring ongoing updates and interpretation.