Causal Map Ethical Principles

We are committed to supporting rigorous, ethical qualitative research. These principles guide how we design Causal Map, how we support our users, and how we operate our business.

1. Global equity

We believe causal mapping should be accessible to researchers and evaluators everywhere, regardless of geography or income. We:

  • Offer substantial discounts to researchers in the Global South and lower-income countries.
  • Provide free access to students and non-profit researchers upon application.
  • Make our educational resources freely available in the Causal Mapping Guide.
  • Support capacity-building in qualitative research through our training and consultancy.

2. Modern slavery and human rights

We are committed to ethical labour practices and human rights. We:

  • Ensure all suppliers meet international labour standards (no forced labour, child labour, or exploitation).
  • Conduct regular audits of our supply chain.
  • Do not work with organisations or countries under sanctions where prohibited by law.
  • Support research on social and economic rights through discounted or free access to Causal Map.

3. AI transparency

We use AI features to assist users, but never as a replacement for human judgment. We:

  • Are transparent about how AI works and where it is used in Causal Map.
  • Make all AI features optional — users can code entirely manually if they prefer.
  • Always require human review and approval of AI suggestions before they are saved.
  • Do not use AI to make decisions for users; it suggests only.
  • Regularly audit AI features for bias and errors.
  • Comply with the UK AI Bill and GDPR requirements on automated decision-making.

4. Evidence provenance

We support rigorous evidence standards. We:

  • Encourage users to code transparently, documenting coding rules and disagreements.
  • Support users in distinguishing between their data, their interpretation, and their analysis.
  • Recommend best-practice workflows for qualitative research (including inter-coder agreement checks, code audit trails, and stakeholder validation).
  • Provide tools to track and record your coding decisions and methodology.
  • Do not claim that Causal Map “generates” evidence; it helps you analyse and visualise the evidence you collect.

5. Right to be wrong

Qualitative research is interpretive. Different researchers may code the same data differently. We:

  • Support multiple coding schemes and parallel analysis.
  • Allow users to compare and visualise disagreement in their data.
  • Encourage users to treat disagreement as data, not as error.
  • Do not impose a single “correct” way to code causal narratives.
  • Support research on divergent perspectives and contested causality.

6. Participant status

People who provide data (research participants) deserve respect and protection. We:

  • Encourage users to consider the ethical implications of analysing and sharing causal narratives.
  • Provide privacy-preserving tools to anonymise and aggregate findings.
  • Recommend that users maintain informed consent and data governance agreements with participants.
  • Support users in fulfilling their ethical obligations to research participants.
  • Do not analyse or extract data without the user’s explicit instruction.

7. Data protection

We protect the personal data and research data entrusted to us. We:

  • Encrypt all data at rest and in transit using industry-standard security.
  • Do not sell, share, or use your data for any purpose except providing the Service.
  • Comply with GDPR, UK Data Protection Act 2018, and other applicable data protection laws.
  • Allow you to download, export, or delete your data at any time.
  • Minimise personal data collection; we do not require names, institutions, or other PII unless you choose to provide it.
  • Conduct regular security audits and penetration tests.
  • Have a responsible disclosure policy for security vulnerabilities.

Putting these principles into practice

These principles are not static. We evolve them based on user feedback, emerging technologies and ethical frameworks, and our own learning. We:

  • Review these principles annually and update them as necessary.
  • Consult with users, researchers, and ethics experts in updates.
  • Are transparent when we fail to meet these principles and commit to improvement.
  • Encourage users to hold us accountable.

Questions?

If you have concerns about our ethical practices or questions about how Causal Map aligns with your research ethics, please contact hello@causalmap.app.