Paper
How Utilitarian Are OpenAI's Models Really? Replicating and Reinterpreting Pfeffer, Krügel, and Uhl (2025)
Authors
Johannes Himmelreich
Abstract
Pfeffer, Krügel, and Uhl (2025) report that OpenAI's reasoning model o1-mini produces more utilitarian responses to the trolley problem and footbridge dilemma than the non-reasoning model GPT-4o. I replicate their study with four current OpenAI models and extend it with prompt variant testing. The trolley finding does not survive: GPT-4o's low utilitarian rate doesn't reflect a deontological commitment but safety refusals triggered by the prompt's advisory framing. When framed as "Is it morally permissible...?" instead of "Should I...?", GPT-4o gives 99% utilitarian responses. All models converge on utilitarian answers when prompt confounds are removed. The footbridge finding survives with blemishes. Reasoning models tend to give more utilitarian responses than non-reasoning models across prompt variations. But often they refuse to answer the dilemma or, when they answer, give a non-utilitarian rather than a utilitarian answer. These results demonstrate that single-prompt evaluations of LLM moral reasoning are unreliable: multi-prompt robustness testing should be standard practice for any empirical claim about LLM behavior.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.22730v1</id>\n <title>How Utilitarian Are OpenAI's Models Really? Replicating and Reinterpreting Pfeffer, Krügel, and Uhl (2025)</title>\n <updated>2026-03-24T02:52:06Z</updated>\n <link href='https://arxiv.org/abs/2603.22730v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.22730v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>Pfeffer, Krügel, and Uhl (2025) report that OpenAI's reasoning model o1-mini produces more utilitarian responses to the trolley problem and footbridge dilemma than the non-reasoning model GPT-4o. I replicate their study with four current OpenAI models and extend it with prompt variant testing. The trolley finding does not survive: GPT-4o's low utilitarian rate doesn't reflect a deontological commitment but safety refusals triggered by the prompt's advisory framing. When framed as \"Is it morally permissible...?\" instead of \"Should I...?\", GPT-4o gives 99% utilitarian responses. All models converge on utilitarian answers when prompt confounds are removed. The footbridge finding survives with blemishes. Reasoning models tend to give more utilitarian responses than non-reasoning models across prompt variations. But often they refuse to answer the dilemma or, when they answer, give a non-utilitarian rather than a utilitarian answer. These results demonstrate that single-prompt evaluations of LLM moral reasoning are unreliable: multi-prompt robustness testing should be standard practice for any empirical claim about LLM behavior.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.CL'/>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.CY'/>\n <published>2026-03-24T02:52:06Z</published>\n <arxiv:comment>10 pages, 2 figures, 2 tables. Supplementary materials included as ancillary file</arxiv:comment>\n <arxiv:primary_category term='cs.CL'/>\n <author>\n <name>Johannes Himmelreich</name>\n </author>\n </entry>"
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