The Computational Metaphor, comparing the brain to the computer and vice versa, is the most prominent metaphor in neuroscience and artificial intelligence (AI). Its appropriateness is highly debated in both fields, particularly with regards to whether it is useful for the advancement of science and technology. Considerably less attention, however, has been devoted to how the Computational Metaphor is used outside of the lab, and particularly how it may shape society's interactions with AI. As such, recently publicized concerns over AI's role in perpetuating racism, genderism, and ableism suggest that the term "artificial intelligence" is misplaced, and that a new lexicon is needed to describe these computational systems. Thus, there is an essential question about the Computational Metaphor that is rarely asked by neuroscientists: whom does it help and whom does it harm? This essay invites the neuroscience community to consider the social implications of the field's most controversial metaphor.

AI. Specifically, we address three Just how little can be illustrated by the reaction to Winograd and Flores's (1986) recent book Understanding Computers and Cognition. In personal comments, the book and its authors have been savaged. Published comments are, of course, more temperate (Vellino et al. 1987) but still reveal the hypersensitivity of the Penrose's (1989) even more recent book The Emperor's New Mind have been observed. Like Suchman (1987) and Clancey (1987), we feel that insights of significant value are to be gained from an objective consideration of traditional and alternative perspectives. Some efforts in this direction are evident (Haugeland [1985], Hill [1989], and Born [1987], for example), but the issue requires additional and ongoing attention.

As computers gain speed and accomplish dazzling feats like defeating the world's masters at games of chess and Go, some of the planet's brightest minds -- Elon Musk and Stephen Hawking among them -- warn that we human beings may find ourselves obsolete. Further, a kind of artificial intelligence arms race may come to dominate geopolitics, rewarding the owners of the best AI mining the biggest pools of "big data" -- most likely, as a result of its sheer size, China. Or consider another dire consequence: As AI-driven robots replace more and more workers, from truck drivers to insurance adjusters, loan officers and any number of other white-collar occupations, unemployment will rise. Should we imagine a utopia filled with gratifying leisure activities or a feudal dystopia in which a wealthy elite hold the few precious jobs unsuitable for computers? But the terms of the debate thus far are confused. The recent advances in AI are impressive, and the future prospects for the technology are truly amazing.

Previous examination of the computational metaphor exposed behavior inconsistent with that expected of metaphors in general. Specifically, despite demonstrated dissimilarity in the referents of brains (minds) and computers, the metaphor persists, not dissolves. Seeking an explanation of this behavior led to the conclusion that the computational metaphor is not truly a metaphor at all. Instead, it is a kind of shorthand expression, a label, for a set of philosophical presuppositions. Although such an examination might seem to be a straightforward and uncomplicated endeavor, it is not--primarily because of the situation aptly captured and summarized by Johnson (1990): The idea of the brain as an information processor--a machine made from matter manipulating blips of energy according to fathomable rules--has come to dominate neuroscience….

This article picks up the call for a reflective examination of the prevailing computational metaphor of AI (and philosophical presuppositions behind it) by sketching alternatives that might serve as seeds for discussion-specifically, the seven alternatives introduced in our previous article (see "AI Magazine, spring 1991). The relative strengths and weaknesses of the alternatives are contrasted with those of the computational metaphor.

This article picks up the call for a reflective examination of the prevailing computational metaphor of AI (and philosophical presuppositions behind it) by sketching alternatives that might serve as seeds for discussion-specifically, the seven alternatives introduced in our previous article (see "AI Magazine, spring 1991). The relative strengths and weaknesses of the alternatives are contrasted with those of the computational metaphor.

Advocates and critics of AI have long engaged in a debate that has generated a great deal of heat but little light. Whatever the merits of specific contributions to this ongoing debate, the fact that it continues points to the need for a reflective examination of the foundations of AI by its active practitioners. Following the lead of Earl MacCormac, we hope to advance such a reflective examination by considering questions of metaphor in science and the computational metaphor in AI. Specifically, we address three issues: the role of metaphor in science and AI, an examination of the computational metaphor, and an introduction to the possibility and potential value of using alternative metaphors as a foundation for AI theory.

Advocates and critics of AI have long engaged in a debate that has generated a great deal of heat but little light. Whatever the merits of specific contributions to this ongoing debate, the fact that it continues points to the need for a reflective examination of the foundations of AI by its active practitioners. Following the lead of Earl MacCormac, we hope to advance such a reflective examination by considering questions of metaphor in science and the computational metaphor in AI. Specifically, we address three issues: the role of metaphor in science and AI, an examination of the computational metaphor, and an introduction to the possibility and potential value of using alternative metaphors as a foundation for AI theory.