  Emerging methods for participatory algorithm design have proposed collecting
and aggregating individual stakeholder preferences to create algorithmic
systems that account for those stakeholders' values. Using algorithmic student
assignment as a case study, we argue that optimizing for individual preference
satisfaction in the distribution of limited resources may actually inhibit
progress towards social and distributive justice. Individual preferences can be
a useful signal but should be expanded to support more expressive and inclusive
forms of democratic participation.
