The Rhetoric and Reality of Anthropomorphism in Artificial Intelligence
By David Watson. 2019
120 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Cognitive Neuroscience, Social Psychology, Philosophy
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Elizabeth Clark
- Sebastian Gehrmann
- Thibault Sellam
- Iason Gabriel
- Tom Everitt
- Geoffrey Irving
- Vladimir Mikulik
- Laura Weidinger
In Cognitive Neuroscience
- Iason Gabriel
- Kathinka Evers
- Aaron Hertzmann
- Tom Everitt
- Sebastian Gehrmann
- Arleen Salles
- Michele Farisco
In Social Psychology
- Iason Gabriel
- Kathinka Evers
- Vladimir Mikulik
- Tom Everitt
- Geoffrey Irving
- Arun Ross
- Morgan R. Frank
- Arleen Salles
In Philosophy
- Iason Gabriel
- Elizabeth Clark
- Vladimir Mikulik
- Tom Everitt
- Geoffrey Irving
- Sebastian Gehrmann
- Zachary Kenton
- Laura Weidinger
Citations in the corpus
- Repairing the Cracked Foundation: A Survey of Obstacles in Evaluation Practices for Generated Text (2022, background): “This presentation, alongside the anthropomorphic bias of deep learning models (Watson, 2019), can perpetuate these opinions including harmful stereotypes.”
- Alignment of Language Agents (2021, background): “• Undue trust being placed in the system, especially as it communicates with humans in natural language, and could easily be mistaken for a human (Proudfoot, 2011; Watson, 2019).”
- Trust in AI and Its Role in the Acceptance of AI Technologies (2022, background): “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).”
- Anthropomorphism in AI (2020, background): “DNNs do not recreate human intelligence: they introduce a new mode of inference that is better than ours in some respects and worse in others (Watson 2019b).”
- The explanation game: a formal framework for interpretable machine learning (2020, unknown): “First, the talk of algorithmic “decisions” is an anthropomorphic trope granting statistical models a degree of autonomy that dangerously downplays the true role of human agency in sociotechnical systems (Watson 2019).”
- Art and the science of generative AI (2023, background): “Anthropomorphizing AI can pose challenges to the ethical usage of this technology [12].”