Tournesol: A quest for a large, secure and trustworthy database of reliable human judgments
L\^e-Nguy\^en Hoang, Louis Faucon, Aidan Jungo, Sergei Volodin, Dalia, Papuc, Orfeas Liossatos, Ben Crulis, Mariame Tighanimine, Isabela Constantin,, Anastasiia Kucherenko, Alexandre Maurer, Felix Grimberg, Vlad Nitu, Chris, Vossen, S\'ebastien Rouault, El-Mahdi El-Mhamdi

TL;DR
Tournesol is an open-source platform designed to collect a large, secure, and trustworthy database of human judgments to improve the safety and ethics of large-scale algorithms that influence societal information dissemination.
Contribution
The paper introduces Tournesol, a novel platform for gathering reliable human judgments to serve as a foundational dataset for ethical algorithm development.
Findings
Proposes a secure, open platform for human judgment collection.
Highlights challenges in building trustworthy datasets.
Suggests this dataset as essential for safe algorithm design.
Abstract
Today's large-scale algorithms have become immensely influential, as they recommend and moderate the content that billions of humans are exposed to on a daily basis. They are the de-facto regulators of our societies' information diet, from shaping opinions on public health to organizing groups for social movements. This creates serious concerns, but also great opportunities to promote quality information. Addressing the concerns and seizing the opportunities is a challenging, enormous and fabulous endeavor, as intuitively appealing ideas often come with unwanted {\it side effects}, and as it requires us to think about what we deeply prefer. Understanding how today's large-scale algorithms are built is critical to determine what interventions will be most effective. Given that these algorithms rely heavily on {\it machine learning}, we make the following key observation: \emph{any…
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Taxonomy
TopicsEthics and Social Impacts of AI · Adversarial Robustness in Machine Learning · Misinformation and Its Impacts
