Deceptive Appearances: the Turing Test, Response-Dependence, and Intelligence as an Emotional Concept

Citation

Wheeler M (2020) Deceptive Appearances: the Turing Test, Response-Dependence, and Intelligence as an Emotional Concept. Minds and Machines, 30 (4), pp. 513-532. https://doi.org/10.1007/s11023-020-09533-8

Abstract
The Turing Test is routinely understood as a behaviourist test for machine intelligence. Diane Proudfoot (Rethinking Turing’s Test, Journal of Philosophy, 2013) has argued for an alternative interpretation. According to Proudfoot, Turing’s claim that intelligence is what he calls ‘an emotional concept’ indicates that he conceived of intelligence in response-dependence terms. As she puts it: ‘Turing’s criterion for “thinking” is…: x is intelligent (or thinks) if in the actual world, in an unrestricted computer-imitates-human game, x appears intelligent to an average interrogator’. The role of the famous test is thus to provide the conditions in which to examine the average interrogator’s responses. I shall argue that Proudfoot’s analysis falls short. The philosophical literature contains two main models of response-dependence, what I shall call the transparency model and the reference-fixing model. Proudfoot resists the thought that Turing might have endorsed one of these models to the exclusion of the other. But the details of her own analysis indicate that she is, in fact, committed to the claim that Turing’s account of intelligence is grounded in a transparency model, rather than a reference-fixing one. By contrast, I shall argue that while Turing did indeed conceive of intelligence in response-dependence terms, his account is grounded in a reference-fixing model, rather than a transparency one. This is fortunate (for Turing), because, as an account of intelligence, the transparency model is arguably problematic in a way that the reference-fixing model isn’t.

Keywords
Artificial intelligence (AI); Imitation game; Machine intelligence; Response-dependence; The Turing Test

Journal
Minds and Machines: Volume 30, Issue 4