Abstract
How has multiplicity superseded impossibility in philosophical challenges to artificial consciousness? I assess a trajectory in recent debates on artificial consciousness, in which metaphysical and explanatory challenges to the possibility of building conscious machines lead to epistemological concerns about the multiplicity underlying ‘what it is like’ to be a conscious creature or be in a conscious state. First, I analyse earlier challenges which claim that phenomenal consciousness cannot arise, or cannot be built, in machines. These are based on Block’s Chinese Nation and Chalmers’ Hard Problem. To defuse such challenges, theorists of artificial consciousness can appeal to empirical methods and models of explanation. Second, I explain why this naturalistic approach produces an epistemological puzzle on the role of biological properties in phenomenal consciousness. Neither behavioural tests nor theoretical inferences seem to settle whether our machines are conscious. Third, I evaluate whether the new challenge can be managed through a more fine-grained taxonomy of conscious states. This strategy is supported by the development of similar taxonomies for biological species and animal consciousness. Although it makes sense of some current models of artificial consciousness, it raises questions about their subjective and moral significance.
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Notes
- 1.
I learnt especially from the responses offered in Prinz (2003) and Gamez (2008). I have put aside challenges based on Searle’s Chinese Room thought experiment: they are analysed exhaustively in the literature on artificial consciousness, with what looks to be diminishing returns. One response to these challenges can be modelled after my response in (a).
- 2.
This is a common idealisation in the thought experiment. In reality, we will find more than one psychological level and more than one computational one (Prinz 2003, 120–1). During chip replacement, we are more likely to keep constant processes at less fine-grained psychological and computational levels. The epistemological difficulty with testing remains, though it is made more complicated. Elsewhere, in Chin (2016), I analyse more complicated versions of the multiple-kinds problem in consciousness science; see also Irvine (2013), Chap. 6.
- 3.
- 4.
This problem is analysed by both biologists and philosophers: see the surveys in Coyne and Orr (2004); Cracraft (2000); Ereshefsky (2010, 2017); Richards (2010). I also learnt from the analysis in LaPorte (2004), though we come to different conclusions. Richards (2010) argues that the problem goes back to pre-Darwinian times: Darwin himself was confronted by ‘a multiplicity of species concepts’ (75).
- 5.
The BSC defines species as ‘groups of interbreeding natural populations that are reproductively isolated from other such groups’ (Mayr 1969). The PSC defines them as the ‘smallest diagnosable cluster of individual organisms within which there is a parental pattern of ancestry and descent’ (Cracraft 1983). The ESC defines them as ‘a lineage (or a closely related set of lineages) which occupies an adaptive zone minimally different from that of any other lineage in its range and which evolves separate from all lineages outside its range’ (Van Valen 1976).
- 6.
As Cracraft (2000) warns, ‘the notion of “best” is always relative’ (10). He urges us to ‘look hard at the context of what best might mean’, including how general in application a definition is meant to be, and whether a more general definition is always more useful.
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Acknowledgements
I thank Arzu Gokmen, Michael Prinzing, and Kaine Yeo for their suggestions. Abhishek Mishra, Susan Schneider, Paul Schweitzer, and Alexandra Serrenti commented on the talk. This research was supported by an NUS Early Career Award.
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Chin, C. (2018). Artificial Consciousness: From Impossibility to Multiplicity. In: Müller, V. (eds) Philosophy and Theory of Artificial Intelligence 2017. PT-AI 2017. Studies in Applied Philosophy, Epistemology and Rational Ethics, vol 44. Springer, Cham. https://doi.org/10.1007/978-3-319-96448-5_1
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