What If I Don't Like Any Of The Choices? The Limits of Preference Elicitation for Participatory Algorithm Design


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.

Participatory Approaches to Machine Learning, a Workshop at ICML 2020