Manifesto for Scientifically Sound Artificial Intelligence Towards an Artificial Intelligence Serving Scientific Rigor

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Auteur principal: Febba, Michel
Format: Recurso digital
Langue:anglais
Publié: Zenodo 2025
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author Febba, Michel
author_facet Febba, Michel
contents <p>This manifesto establishes the fundamental principles for an artificial intelligence worthy of scientific rigor. It emphasizes transparency, reproducibility, reference traceability, and the use of real or clearly labeled data. AI must produce only accurate responses or remain silent, without unnecessary embellishments, hidden text, or phantom citations.<br><br>The document includes a concrete example illustrating the application of these principles: the cosmological pipeline TRUE 7.1 – Corrigendum and Synthetic Version v3. It demonstrates that it is possible to publish data, code, and scientific results in a fully transparent manner.<br><br></p>
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spellingShingle Manifesto for Scientifically Sound Artificial Intelligence Towards an Artificial Intelligence Serving Scientific Rigor
Febba, Michel
artificial intelligence, scientific transparency, reproducibility, open science, clean AI, reliable AI
<p>This manifesto establishes the fundamental principles for an artificial intelligence worthy of scientific rigor. It emphasizes transparency, reproducibility, reference traceability, and the use of real or clearly labeled data. AI must produce only accurate responses or remain silent, without unnecessary embellishments, hidden text, or phantom citations.<br><br>The document includes a concrete example illustrating the application of these principles: the cosmological pipeline TRUE 7.1 – Corrigendum and Synthetic Version v3. It demonstrates that it is possible to publish data, code, and scientific results in a fully transparent manner.<br><br></p>
title Manifesto for Scientifically Sound Artificial Intelligence Towards an Artificial Intelligence Serving Scientific Rigor
topic artificial intelligence, scientific transparency, reproducibility, open science, clean AI, reliable AI
url https://doi.org/10.5281/zenodo.17776147