  Recommendation algorithms play an increasingly central role in our
information ecosystem. Yet, so far, they are mostly designed, parameterized and
updated unilaterally by private groups or governmental authorities, based on
insecure data from increasingly many fake accounts. In this paper, we present
an end-to-end permissionless collaborative algorithmic governance pipeline with
security guarantees, which is deployed on the open-source platform
https://tournesol.app. Our pipeline has essentially four steps. First, voting
rights are assigned to the contributors, based on Sybil-resilient email domains
and on a novel secure trust propagation algorithm. Second, a generalized
Bradley-Terry model turns contributors' pairwise alternative comparisons into
scores. Third, contributors' scores are collaboratively scaled, by an
adaptation of the robust sparse voting solution Mehestan. Finally, scaled
scores are post-processed and securely aggregated into human-readable global
scores, which are used for recommendation and display. We believe that our
pipeline lays an appealing foundation for any collaborative, effective,
scalable, fair, interpretable and secure algorithmic governance.
