Paper Overview
Field: ML Authors: Kevin Kingslin, Anish Natekar, Ashutosh Ranjan Published: 2026-06-26 arXiv: 2606.28294
Summary
Preference-based alignment often struggles to capture the reasoning that underlies human judgments. Many evaluations rely on multiple interacting criteria, yet pairwise labels reveal only the final choice rather than the considerations that shape preferences. Inverse Constitutional AI (ICAI) improves interpretability in decision making by summarizing preferences into natural-language principles, but its single-pass explanations miss much of the nuance involved in complex decisions.
The authors introduce Democratic ICAI, a novel approach that gathers multiple competing rationales through structured persona debate, offering a broader and more expressive account of the factors influencing each comparison. From these richer signals, they derive clearer and more comprehensive steering principles and use them to guide decision modeling via LLM-based and decision-tree judges.
Results
Experiments on the creative preference benchmarks MuCE-Pref and LiTBench, spanning multiple creative task categories, show that Democratic ICAI produces more faithful preference structures. Relative to deliberative prompting and principle-based baselines, it improves average preference prediction across tasks while producing constitutions that LLM annotators prefer.
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