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 algorithm trained on uncontrolled data must not be
trusted}. Indeed, a malicious entity could take control over the data, poison
it with dangerously manipulative fabricated inputs, and thereby make the
trained algorithm extremely unsafe. We thus argue that the first step towards
safe and ethical large-scale algorithms must be the collection of a large,
secure and trustworthy dataset of reliable human judgments.

To achieve this, we introduce emph{Tournesol}, an open source platform
available at url{}. Tournesol aims to collect a large
database of human judgments on what algorithms ought to widely recommend (and
what they ought to stop widely recommending). We outline the structure of the
Tournesol database, the key features of the Tournesol platform and the main
hurdles that must be overcome to make it a successful project. Most
importantly, we argue that, if successful, Tournesol may then serve as the
essential foundation for any safe and ethical large-scale algorithm.

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