# Attributions toward Artificial Agents in a modified Moral Turing Test

arXiv: https://arxiv.org/abs/2406.11854 · HTML: https://mlsanity.com/paper/2406.11854/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 60 references that are in the map.
- First posted on arXiv 3 Apr 2024.

## Builds on (most cited first, 10 of 10)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models](https://mlsanity.com/paper/2305.08283/) (2023)
- [The political ideology of conversational AI: Converging evidence on ChatGPT's pro-environmental, left-libertarian orientation](https://mlsanity.com/paper/2301.01768/) (2023)
- [A Categorical Archive of ChatGPT Failures](https://mlsanity.com/paper/2302.03494/) (2023)
- [Can Machines Learn Morality? The Delphi Experiment](https://mlsanity.com/paper/2110.07574/) (2021)
- [The social dilemma of autonomous vehicles](https://mlsanity.com/paper/1510.03346/) (2015)
- [Large Language Models and the Reverse Turing Test](https://mlsanity.com/paper/2207.14382/) (2022)
- [Human or Not? A Gamified Approach to the Turing Test](https://mlsanity.com/paper/2305.20010/) (2023)
- [Taking Turing by Surprise? Designing Digital Computers for morally-loaded contexts](https://mlsanity.com/paper/1803.04548/) (2018)

## Built on by (most cited first, 18 of 18)

- [Large Language Models Do Not Simulate Human Psychology](https://mlsanity.com/paper/2508.06950/) (2025)
- [The Potential and Challenges of Evaluating Attitudes, Opinions, and Values in Large Language Models](https://mlsanity.com/paper/2406.11096/) (2024)
- [MoralReason: Generalizable Moral Decision Alignment For LLM Agents Using Reasoning-Level Reinforcement Learning](https://mlsanity.com/paper/2511.12271/) (2025)
- [On the Context Sensitivity of LLM Moral Judgment](https://mlsanity.com/paper/2603.23114/) (2026)
- [Psychomatics -- A Multidisciplinary Framework for Understanding Artificial Minds](https://mlsanity.com/paper/2407.16444/) (2024)
- [Beyond Ethical Alignment: Evaluating LLMs as Artificial Moral Assistants](https://mlsanity.com/paper/2508.12754/) (2025)
- [Incoherent Values? Probing LLM Preferences Through Parametric Variation](https://mlsanity.com/paper/2606.21102/) (2026)
- [BERT's Conceptual Cartography: Mapping the Landscapes of Meaning](https://mlsanity.com/paper/2408.07190/) (2024)
- [Contemplative Artificial Intelligence](https://mlsanity.com/paper/2504.15125/) (2025)
- [Free Lunch for User Experience: Crowdsourcing Agents for Scalable User Studies](https://mlsanity.com/paper/2505.22981/) (2025)
- [The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment](https://mlsanity.com/paper/2607.05552/) (2026)
- [Black Box Deployed -- Functional Criteria for Artificial Moral Agents in the LLM Era](https://mlsanity.com/paper/2507.13175/) (2025)
- [Profiling learners' affective engagement: Emotion AI, intercultural pragmatics, and language learning](https://mlsanity.com/paper/2603.20479/) (2026)
- [Are LLMs Bad at Moral Reasoning?](https://mlsanity.com/paper/2606.11635/) (2026)
- [Auditing LLM-Governed Social Robots with Culture-Specific Moral Gradients](https://mlsanity.com/paper/2606.28345/) (2026)
- [The Storyteller in the Model: Narrative Pattern Inheritance, Escalation Dynamics, and Alignment Governance in LLMs](https://mlsanity.com/paper/2607.20449/) (2026)
- [Talking Past the Machine: Morality, Politeness, and Alignment in Human-AI Dialogue](https://mlsanity.com/paper/2609.21401/) (2026)
- [Anthropomorphism in the age of Large Language Models: An overview of potential risks and mitigations](https://mlsanity.com/paper/2609.38486/) (2026)

## Abstract (arXiv)

Advances in artificial intelligence (AI) raise important questions about whether people view moral evaluations by AI systems similarly to human-generated moral evaluations. We conducted a modified Moral Turing Test (m-MTT), inspired by Allen and colleagues' (2000) proposal, by asking people to distinguish real human moral evaluations from those made by a popular advanced AI language model: GPT-4. A representative sample of 299 U.S. adults first rated the quality of moral evaluations when blinded to their source. Remarkably, they rated the AI's moral reasoning as superior in quality to humans' along almost all dimensions, including virtuousness, intelligence, and trustworthiness, consistent with passing what Allen and colleagues call the comparative MTT. Next, when tasked with identifying the source of each evaluation (human or computer), people performed significantly above chance levels. Although the AI did not pass this test, this was not because of its inferior moral reasoning but, potentially, its perceived superiority, among other possible explanations. The emergence of language models capable of producing moral responses perceived as superior in quality to humans' raises concerns that people may uncritically accept potentially harmful moral guidance from AI. This possibility highlights the need for safeguards around generative language models in matters of morality.