Columbia's SPEAR framework reframes AI alignment as an ongoing interactive process after deployment
windx0303 · x · 2026-09-28
A new HCOMP 2026 paper by Tao Long and Lydia B. Chilton (Columbia University), SPEAR: Interactive Human-AI Alignment, argues alignment shouldn't be treated as a pre-deployment optimization pipeline (collect feedback → fine-tune → deploy).
Key claims:
- User intent is rarely complete or stable upfront; people refine goals, discover new uses, and change preferences through interaction.
- Alignment is bidirectional: AI adapts to people while people adapt to AI, updating mental models, trust, and behavior.
- The paper defines alignment as the ongoing interactive process of developing, maintaining, and repairing shared understanding, appropriate agency, and calibrated reliance — making it fundamentally an interaction design problem.
SPEAR proposes five principles framing the core questions of interactive alignment.
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