  Many applications, such as content moderation and recommendation, require
reviewing and scoring a large number of alternatives. Doing so robustly is
however very challenging. Indeed, voters' inputs are inevitably sparse: most
alternatives are only scored by a small fraction of voters. This sparsity
amplifies the effects of biased voters introducing unfairness, and of malicious
voters seeking to hack the voting process by reporting dishonest scores. We
give a precise definition of the problem of robust sparse voting, highlight its
underlying technical challenges, and present a novel voting mechanism
addressing the problem. We prove that, using this mechanism, no voter can have
more than a small parameterizable effect on each alternative's score; a
property we call Lipschitz resilience. We also identify conditions of voters
comparability under which any unanimous preferences can be recovered, even when
each voter provides sparse scores, on a scale that is potentially very
different from any other voter's score scale. Proving these properties required
us to introduce, analyze and carefully compose novel aggregation primitives
which could be of independent interest.
