Abstract
Nowadays, philosophers and scientists tend to agree that, even though human and artificial intelligence work quite differently, they can still illuminate aspects of each other, and knowledge in one domain can inspire progress in the other. For instance, the notion of “artificial” or “synthetic” phenomenology has been gaining some traction in recent AI research. In this paper, we ask the question: what (if anything) is the use of thinking about phenomenology in the context of AI, and in particular machine learning? We will isolate one sense of “phenomenology”, namely the sense in which it is commonly understood within analytic philosophy of perception. Then, we will give examples of projects within sensory substitution and restoration science that rely heavily on machine learning and to which, according to us, phenomenology in the sense specified makes a relevant contribution. Finally, we will shed some light on what this contribution looks like and why it is important.
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Notes
The hard problem of consciousness is contrasted with so-called “easy problems” of consciousness– the kinds of problems science currently has the resources (at least in principle) to explain.
Although mechanistic explanation was traditionally associated with the rejection of functional (in particular, teleological) explanation, explanations in the psychological sciences are replete with functions and mechanisms working in tandem (Craver, 2013). The sorts of functions of interest to Cummins were specifically causal, rather than teleological or etiological functions, i.e. a la Millikan (1989) and Dretske (1988). Causal functions are determined by an analysis of what a system or phenomenon does with some containing system. Some mechanists defend the use of functional descriptions as they assist in the discovery of mechanisms (Bechtel, 2008; Craver, 2001). But mechanistic explanation is also both compatible with and aided by considerations of biological or etiological functions (Block, 1971; Craver, 2013; McClamrock, 1993).
Some other philosophers however suggest that phenomenology has no role to play in individuating the senses (e.g. Keeley 2002).
The claim is not that the sensation itself is what makes the experience unpleasant. This is debatable (see e.g. Aydede & Fulkerson 2019). Rather, the claim is that the functional role of sensory affect requires a conscious sensation to which a valence is attached. In learning, for instance, the sensation is associated with some stimulus such that the affective response becomes associated with that stimulus. Conscious sensation thus has a role in explanations that call on sensory affect.
However, those sympathetic with Kim (1992) might wield the causal exclusion argument and insist that sensory phenomenology is merely epiphenomenal, and thus it cannot itself be the cause or realizer of these functions. We have two lines of reply. The first is metaphysical: it’s not obvious that we should accept Kim’s causal exclusion argument. Several philosophers have appealed to Woodward’s interventionist account of causation to rebut the “master argument for epiphenomenalism” based on Kim’s causal exclusion argument (e.g. Rescorla 2014; Shapiro, 2010; Shapiro & Sober, 2007). We think such an appeal could be made to defend sensory phenomenology from an epiphenomenal fate, but we will not provide such a defense here. For now, it is sufficient to show that it is not a given that sensory phenomenology is epiphenomenal. The second line of reply is epistemological and brief. Even if it were to turn out that causal exclusion is irresistible, until sensory phenomenology has been successfully reduced, it will be necessary to appeal to it in explanations of behavior. Indeed, we will rely on sensory phenomenology in the tests we use to discover the neural basis of sensory phenomenology.
For a recent discussion of whether SS devices should be understood as substituting or restoring the original sense modality, see Pence (2020).
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Buccella, A., Springle, A. Phenomenology: What’s AI got to do with it?. Phenom Cogn Sci 22, 621–636 (2023). https://doi.org/10.1007/s11097-022-09833-7
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DOI: https://doi.org/10.1007/s11097-022-09833-7