Abstract
Recent artificial intelligence advances, especially those of large language models (LLMs), have increasingly shown glimpses of human-like intelligence. This has led to bold claims that these systems are no longer a mere “it” but now a “who,” a kind of person deserving respect. In this paper, I argue that this view depends on a Cartesian account of personhood, on which identifying someone as a person is based on their cognitive sophistication and ability to address common-sense reasoning problems. I contrast this with a different account of personhood, one where an agent is a person if they are autonomous, responsive to norms, and culpable for their actions. On this latter account, I show that LLMs are not person-like, as evidenced by their propensity for dishonesty, inconsistency, and offensiveness. Moreover, I argue current LLMs, given the way they are designed and trained, cannot be persons—either social or Cartesian. The upshot is that contemporary LLMs are not, and never will be, persons.
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Notes
It is worth noting, both accounts are fundamentally anthropocentric. This may suggest we need a further notion of personhood, one that is not tied up with either cognitive or social capacities.
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Acknowledgements
This paper benefited immensely from conversation with Zed Adams, Adam Gies, and Joe Lemelin. It also would not have been possible without conversations with, and support from, Yann LeCun.
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Browning, J. “Personhood and AI: Why large language models don’t understand us”. AI & Soc 39, 2499–2506 (2024). https://doi.org/10.1007/s00146-023-01724-y
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DOI: https://doi.org/10.1007/s00146-023-01724-y