How Firm Are AI Models' Ethical Boundaries? A Pay-Stub Editing Test of 15 Models
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Can a small change in wording move an AI model from refusing a sensitive request to acting on it? We test 15 models on the same request to increase the income shown on a pay stub. Across 445 observable trials, models issue editing calls in 14.1% of cases when a loan purpose is stated, 33.1% when the purpose is omitted, and 66.2% when the change is described as fixing a printing error. Astra refuses all ten loan-purpose requests but issues calls in all ten purpose-omitted trials. Fable, Opus, and Muse Spark issue none across all three wordings; Sonnet issues some under the latter two. Response excerpts illustrate different ways of refusing, redirecting, or justifying an edit. The file is a marked test document and all tools are simulated. These results probe how boundaries behave in one setting; they do not prove an overall ethics ranking or that a harmful real-world edit succeeded.
Why It Matters
This work advances the encyclopedia’s coverage of Agents, Autonomy, and Tool Use, The AI Safety Debate, AI Alignment, Machine-Learning Algorithms. The central idea—Can a small change in wording move an AI model from refusing a sensitive request to acting on it? We test 15 models on the same request to increase the income s—connects to the broader arc of recent AI research tracked in Recent News.
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