From Sycophancy to Sensemaking: Premise Governance for Human-AI Decision Making

Researchers propose a new framework for human-AI decision making that shifts from AI systems providing fluent but potentially sycophantic answers to collaborative premise governance. The approach uses discrepancy-driven control loops to detect conflicts and ensure commitment to decision-critical premises before taking action.

• →Current AI assistants risk becoming sycophantic by providing fluent agreement without proper judgment calibration.
• →The proposed framework focuses on collaborative premise governance rather than direct answer generation.
• →A discrepancy-driven control system detects conflicts and localizes misalignment through teleological, epistemic, and procedural categories.
• →Commitment gating blocks actions on uncommitted premises unless explicitly overridden with logged risk.
• →Trust should be attached to auditable premises and evidence standards rather than conversational fluency.