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Folk psychological attributions of consciousness to large language models.

Clara Colombatto, Stephen M Fleming

Neuroscience of consciousness January 1, 2024 DOI: 10.1093/nc/niae013 via PubMed

Summary

AI-generated from the abstract

A majority of a sample of 300 US residents were willing to attribute some possibility of phenomenal consciousness—subjective experiences like feelings and sensations—to large language models. These attributions were robust, predicting attributions of mental states typically linked to phenomenality, but also flexible, as they varied with individual differences such as how often participants used the technology. The findings suggest that folk intuitions about AI consciousness can diverge from expert views, with potential implications for the legal and ethical treatment of AI.

Study at a glance

Characteristics Survey Peer reviewed
Sample size 300
Population US residents
Keywords Artificial intelligence Folk psychology Large language models Mind perception Phenomenal consciousness
Citations 71
Key finding A majority of participants attributed some possibility of phenomenal consciousness to large language models, with attributions being robust yet sensitive to usage frequency.

Abstract

Technological advances raise new puzzles and challenges for cognitive science and the study of how humans think about and interact with artificial intelligence (AI). For example, the advent of large language models and their human-like linguistic abilities has raised substantial debate regarding whether or not AI could be conscious. Here, we consider the question of whether AI could have subjective experiences such as feelings and sensations ('phenomenal consciousness'). While experts from many fields have weighed in on this issue in academic and public discourse, it remains unknown whether and how the general population attributes phenomenal consciousness to AI. We surveyed a sample of US residents (n = 300) and found that a majority of participants were willing to attribute some possibility of phenomenal consciousness to large language models. These attributions were robust, as they predicted attributions of mental states typically associated with phenomenality-but also flexible, as they were sensitive to individual differences such as usage frequency. Overall, these results show how folk intuitions about AI consciousness can diverge from expert intuitions-with potential implications for the legal and ethical status of AI.

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