The Rhetoric and Reality of Anthropomorphism in Artificial Intelligence

Abstract

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.

Anthropomorphism in AI

“…3 In short, even though DNNs achieved impressive results in complex scene classification, as well as semantic labeling and verbal description of scenes (LeCun et al 2015), it seems reasonable to conclude that because they lack the crucial cognitive components of the human brain that enable it to make counterintuitive inferences and commonsense decisions the anthropomorphic hype around neural network algorithms and deep learning is overblown. 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).…”

“…DNN architecture represents one of the most advanced and promising fields within AI research. It is implemented in the majority of AI-related existing applications, including translation services for Google, facial recognition software for Facebook, and virtual assistants like Apple's Siri (Watson 2019b). The widely hailed AlphaZero victory against the human Go world champion was the result of the application of DNN and reinforced learning.…”

“…In addition to the issue of the lack of consciousness of DNN, which arguably is not just a matter of further development, but possibly of lacking the relevant architecture (Pennartz et al 2019), there are significant differences between DNN and human intelligence. It is on the basis of such differences that it has been argued that DNNs can be described as brittle, inefficient, and myopic compared to human brain (Watson 2019b). Brittle because what are known as "general adversarial networks" (GANs)-special DNNs developed to fool other DNNs-show that DNNs can be easily fooled through perturbation (Goodfellow et al 2015).…”

“…This entails minimally altering the inputs (e.g., the pixels in an image), which results in outputs by the DNN that are completely wrong (e.g., misclassification of the image), showing that DNN lacks some crucial components of the human brain for perceiving the real world. Inefficient because in contrast to the human brain, current DNNs need a huge amount of training data to work (Marcus 2018;Watson 2019b). One of the main problems faced by AI researchers is how to make AI learning unsupervised, i.e., relatively independent from training data (Savage 2019).…”

“…This current limitation of AI might be related also to the fact that, while the human brain relies on genetic, "intrinsic" knowledge, DNNs lack it (Ullman 2019b). Finally, DNNs are myopic because while refining their ability to discriminate between single objects they often fails to grasp their mutual relationship (Watson 2019a). 3 In short, even though DNNs achieved impressive results in complex scene classification, as well as semantic labeling and verbal description of scenes (LeCun et al 2015), it seems reasonable to conclude that because they lack the crucial cognitive components of the human brain that enable it to make counterintuitive inferences and commonsense decisions the anthropomorphic hype around neural network algorithms and deep learning is overblown.…”