IntentLint: Supporting Intent Scaffolding and Prompt-time Linting in Human-AI Collaborative Data Analysis
In human-AI collaborative data analysis, as analyses rapidly evolve, the artifacts meant to capture shared understanding often become incomplete or difficult to interpret, leading to undocumented assumptions, cross-user misaligned intent, context-poor prompts, and unwanted agent behaviors. To address these challenges, we introduce a rule-based coordination layer with two interaction mechanisms, intent scaffolding and prompt-time linting, that make analytic intent explicit and actionable during h
Record details
Published: 5 August 2026
Source: arXiv cs.HC
Category: Research
Topics: Safety & alignment · Agents & autonomy
Retrieved: 6 August 2026
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ethics.ai (5 August 2026), “IntentLint: Supporting Intent Scaffolding and Prompt-time Linting in Human-AI Collaborative Data Analysis,” evidence record 16949, https://ethics.ai/record/16949 (originally published by arXiv cs.HC).
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