Internal Pluralism and the Limits of Pairwise Comparisons
A new arXiv preprint examines how internal pluralism—where individuals hold multiple, sometimes conflicting, priorities—can undermine the effectiveness of standard pairwise comparison methods in participatory design and AI alignment. The authors formally model pluralistic preferences and identify two main issues: global priorities like proportionality may not be captured by local comparisons, and forcing decisive answers can cause behavioral distortions. They find that allowing respondents to express indecision can reduce the number of queries needed and improve the accuracy of preference learning.
Why it matters: This work questions foundational assumptions in preference learning for AI alignment and participatory design, suggesting that accounting for internal pluralism could lead to more accurate and interpretable systems.
Full story at: arXiv Computers and Society ↗