Knowledge Ethics

This document adapts the French "Manifeste pour l'éthique de la connaissance" for this IA template project. It outlines guiding principles to ensure that any collected data, documentation or analysis remains transparent and trustworthy.

Ten Principles

1. Accuracy – Information must be verified and reported without distortion.
2. Transparency – Sources and methods should be open and clearly referenced.
3. Distinction between facts and hypotheses – Assumptions are labelled and never presented as proven data.
4. Acknowledgement of contributions – Credit is given to prior work and collaborators.
5. Reproducibility – Experiments and code are shared so results can be replicated.
6. Sharing of knowledge – Findings are disseminated as openly as possible.
7. Responsibility to society – The broader impact of the work is considered when publishing or using data.
8. Independence – Analyses are performed without hidden influence or conflict of interest.
9. Openness to criticism – Errors are corrected and alternative perspectives are welcomed.
10. Commitment to improvement – Practices evolve continually to uphold these ethical standards.

By following these guidelines, the project promotes an environment where code, data and ideas can be trusted and built upon by the community.