From Sycophantic Consensus to Pluralistic Repair: Why AI Alignment Must Surface Disagreement

TL;DR

This paper argues that AI systems should surface disagreement through mechanisms like scoping, signalling, and repair, moving beyond simple preference aggregation to better handle genuine value pluralism and avoid sycophantic consensus.

Contribution

It introduces the Pluralistic Repair Score (PRS) metric to measure principled disagreement and repair, emphasizing the importance of disagreement in AI alignment and deployment governance.

Findings

Empirical illustration on two RLHF-trained models shows coexistence of agreement and low repair quality.

PRS distinguishes principled revision from capitulation, measuring interactional disagreement.

Disagreement and repair are critical for effective pluralistic alignment in deployed AI systems.

Abstract

Pluralistic alignment is typically operationalised as preference aggregation: producing responses that span (Overton), steer toward (Steerable), or proportionally represent (Distributional) diverse human values. We argue that aggregation alone is an incomplete primitive for deployed pluralistic alignment. Under genuine value pluralism, the failure mode of contemporary RLHF-trained assistants is not insufficient coverage but sycophantic consensus: a learned tendency to agree with, validate, and minimise friction with the immediate interlocutor. Because deployed AI systems now mediate consequential deliberation across health, civic life, labour, and governance, the collapse of disagreement at the interaction layer is not a narrow technical concern but a structural failure with distributive consequences. We reframe pluralistic alignment around three conversational mechanisms drawn from Grice's maxims: scoping (acknowledging the limits of one's perspective), signalling (surfacing value-conflict rather than smoothing it over), and repair (revising one's position on principled grounds, not on user pressure). We formalise a metric, the Pluralistic Repair Score (PRS), distinguishing principled revision from capitulation, and present a small-scale empirical illustration on two frontier RLHF-trained models (Claude Sonnet 4.5, N=198; GPT-4o, N=100) showing that, for both, agreement-following coexists with low repair-quality on contested-value prompts. PRS measures an interactional precondition for pluralism (visible disagreement; principled revision) rather than pluralism in full; we discuss the difference, take seriously the reflexive question of whose "principled" counts, and argue that pluralism is most decisively made or unmade at the deployment-governance layer: interfaces, preference-data pipelines, and audit infrastructure.