The Rhetoric and Reality of Anthropomorphism in Artificial Intelligence
Open accessAbstract
Artificial intelligence (AI) has historically been conceptualized in anthropomorphic terms. Some algorithms deploy biomimetic designs in a deliberate attempt to effect a sort of digital isomorphism of the human brain. Others leverage more general learning strategies that happen to coincide with popular theories of cognitive science and social epistemology. In this paper, I challenge the anthropomorphic credentials of the neural network algorithm, whose similarities to human cognition I argue are vastly overstated and narrowly construed. I submit that three alternative supervised learning methods—namely lasso penalties, bagging, and boosting—offer subtler, more interesting analogies to human reasoning as both an individual and a social phenomenon. Despite the temptation to fall back on anthropomorphic tropes when discussing AI, however, I conclude that such rhetoric is at best misleading and at worst downright dangerous. The impulse to humanize algorithms is an obstacle to properly conceptualizing the ethical challenges posed by emerging technologies.
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DNNs are one of the most advanced and promising fields within AI research, with DNN architecture applied in AlphaZero's win and many AI-related applications.
“It is true that DNNs are one of the most advanced and promising fields within AI research, with DNN architecture applied in AlphaZero's famous win over the human Go world champion and a part of many AI-related applications, such as Google translation services, Facebook facial recognition software, and virtual assistants like Apple's Siri [9, 17].”
From Anthropomorphism in AI: hype and fallacy (2024) · cites this paper as [9]
Section 3, “Neural Networks” (page unavailable), says DNNs drove a wave of AI research and power Google translation; the following passage also names Facebook facial recognition and Siri. Another passage describes AlphaZero as a DNN and a Go champion, but does not say it beat a human Go world champion. The paper calls DNN achievements impressive while arguing that the field has been overhyped, so it does not support calling DNNs among AI’s most promising fields without qualification. (LLM, conf=0.86)
Experimental — accuracy not yet measuredDNNs' similarities to human cognition have been seriously overstated and narrowly construed, especially in light of DNNs' considerable shortcomings such as brittleness, inefficiency and myopia.
“David Watson [9], for example, has argued that DNNs' similarities to human cognition have been seriously overstated and narrowly construed, especially in light of DNNs' considerable shortcomings (e.g., brittleness, inefficiency, and myopia).”
From Anthropomorphism in AI: hype and fallacy (2024) · cites this paper as [9]
Even for AI techniques explicitly modelled to resemble human neurons, the similarity to actual human cognition has been described as overstated, possibly leading to dangerous misunderstandings.
“Even for the AI techniques that were explicitly modelled to resemble human neurons, the similarity to actual human cognition has been described as overstated, possibly leading to dangerous misunderstandings [89].”
From Deanthropomorphising NLP: Can a language model be conscious? (2024) · cites this paper as [89]
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“Inspired by human intelligence, artificial intelligence (AI) systems are becoming increasingly adept in their ability to learn, reason, self-correct and emulate human decisions in some domains (Russell et al., 2016; Watson, 2019).”
BackgroundEthical framework for Artificial Intelligence and Digital technologies
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“Even for the AI techniques that were explicitly modelled to resemble human neurons, the similarity to actual human cognition has been described as overstated, possibly leading to dangerous misunderstandings (Watson, 2019).”
- Authors
- Watson, D.
- Affiliations
- University of Oxford (GB)
- Corresponding author
- Watson, David
- ORCID
- Watson, David
- Venue
- Minds and Machines
- Published
- 2019-09
- Volume
- 29
- Issue
- 3
- Pages
- 417-440
- ISSN
- 0924-6495 (ISSN-L)
- 1572-8641
- OpenAlex
- W2973907375
- Cited by
- 148
- References
- 135
- Citations by year
- 2026: 26 · 2025: 28 · 2024: 34 · 2023: 19 · 2022: 16 · 2021: 16 · 2020: 9
- FWCI
- 17.91
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Minds and Machines (publishedVersion, cc-by)
- UCL Discovery (University College London) (submittedVersion)
- PhilPapers (PhilPapers Foundation) (submittedVersion)
- Queen Mary Research Online (Queen Mary University of London) (submittedVersion, cc-by)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Cybernetics and Technology in Society
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- anthropomorphism, artificial intelligence, artificial neural networks, human cognition, biomimetic design, lasso penalty, bagging, boosting, supervised learning, social epistemology, cognitive science, AI ethics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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