If a machine attains consciousness, how could we find out? In this paper, I make three related claims regarding positive tests of machine consciousness. All three claims center on the idea that an AI can be constructed “ad hoc”, that is, with the purpose of satisfying a particular test of consciousness while clearly not being conscious. First, a proposed test of machine consciousness can be legitimate, even if AI can be constructed ad hoc specifically to pass this test. This is (...) underscored by the observation that many, if not all, putative tests of machine consciousness can be passed by non-conscious machines via ad hoc means. Second, we can identify ad hoc AI by taking inspiration from the notion of an ad hoc hypothesis in philosophy of science. Third, given the first and the second claim, the most reliable tests of animal consciousness turn out to be valid and useful positive tests of machine consciousness as well. If a non-ad hoc AI exhibits clusters of cognitive capacities facilitated by consciousness in humans which can be selectively switched off by masking and if it reproduces human behavior in suitably designed double dissociation tasks, we should treat the AI as conscious. (shrink)

Many different methodological approaches have been proposed to infer the presence of consciousness in non-human systems. In this paper, a version of the theory-heavy approach is defended. Theory-heavy approaches rely heavily on considerations from theories of consciousness to make inferences about non-human consciousness. Recently, the theory-heavy approach has been critiqued in the form of Birch's (Noûs, 56(1): 133-153, 2022) dilemma of demandingness and Shevlin's (Mind & Language, 36(2): 297-314, 2021) specificity problem. However, both challenges implicitly assume an inapt characterization of (...) the theory-heavy approach. I argue that an alternative characterization of the approach, what I call a credence-based theory-heavy approach, avoids these challenges. Theorists can generate interpretations of their theory, at different levels of generality, and operationalize these into theory-informed markers. These theory-informed are assigned a likelihood and are used to assess the probability that a target system is conscious. In providing this characterization, and mapping out the possible ways in which a credence-based theory-heavy approach can be fleshed out, the aim is to situate the theory-heavy approach as a more compelling approach than it is currently being perceived as. Our attention, then, needs to towards remaining challenges such as the consensus problem and the problem of calibrating the likelihoods associated with theory-informed markers. I also explore methodological pluralism and assess how the credence-based theory-heavy approach can benefit from other methodological approaches. (shrink)