Motivated by growing interest in the similarities between problems in learning and social choice, SCaLA-26 aims to bring together researchers across these domains to highlight the benefits of collaboration. Recent work has explored theoretical bounds on the learnability of common voting rules alongside experimental evaluation of these bounds, has shown how neural networks can improve properties of voting rules or learn mechanism design, and has raised many questions.
The goal of this workshop is to highlight new connections between social choice and learning algorithms. We seek contributions that demonstrate how either one of these fields can be used to strengthen the other and, more broadly, that combine aspects of the two domains in novel ways. We are interested in a broad range of topics from both disciplines.