Axel Constant

Rapid progress in artificial intelligence (AI) capabilities has drawn fresh attention to the prospect of consciousness in AI. There is an urgent need for rigorous methods to assess AI systems for consciousness, but significant uncertainty about relevant issues in consciousness science. We present a method for assessing AI systems for consciousness that involves exploring what follows from existing or future neuroscientific theories of consciousness. Indicators derived from such theories can be used to inform credences about whether particular AI systems are conscious. This method allows us to make meaningful progress because some influential theories of consciousness, notably including computational functionalist theories, have implications for AI that can be investigated empirically.

The debate on whether psychiatric disorders can be studied as natural kinds has raised controversy, reviving socio-constructionist arguments about the influence of social factors on psychiatric categories. A key concept in this discussion is the “looping effect”, which describes how individuals change in response to their classifications, necessitating revisions to those classifications. We argue that, until now, the broad discussions around the looping effect have greatly failed to integrate the perspectives surrounding clinicians and patients. We examine more closely the dynamic and unstable nature of psychiatric diagnoses by proposing two key hypotheses: first, that understanding the looping effect requires incorporating both clinician and patient viewpoints, and that when done adequately, such an incorporation can facilitate the work of the clinician by creating feedback loops (i.e., the iterative adjustment of clinical interpretations based on patient responses); and second, that epistemic injustices between clinicians and patients can create disruptions in these feedback loops, which we call “looping breaks”, rendering them ineffective. Looping breaks can happen at the clinical level of the relationship between the patient and the clinician or at the nosological level (during the process of revising a classification). We suggest that looping breaks can be caused by a denial or minimization of credibility based on identity prejudice, or due to an epistemic disadvantage, affecting the experiential feedback of patients following the announcement of a diagnosis. To substantiate our claims, we first examine the impact of looping effects in the interaction between patients and clinicians. Second, we investigate the impact of these interactions at the nosological level, on the broader diagnostic framework. We identify epistemic injustices as critical factors that can lead to looping breaks at both levels, thus affecting the stability and validity of psychiatric diagnoses. Our findings underscore the importance of an epistemic approach to the looping effect, emphasizing both knowledge validity and justice in clinician-patient relationships and among clinicians themselves.

This article applies the thesis of the extended mind to ambient smart environments. These systems are characterised by an environment, such as a home or classroom, infused with multiple, highly networked streams of smart technology working in the background, learning about the user and operating without an explicit interface or any intentional sensorimotor engagement from the user. We analyse these systems in the context of work on the “classical” extended mind, characterised by conditions such as “trust and glue” and phenomenal transparency, and find that these conditions are ill-suited to describing our engagement with ambient smart environments. We then draw from the active inference framework, a theory of brain function which casts cognition as a process of embodied uncertainty minimisation, to develop a version of the extended mind grounded in a process ontology, where the boundaries of mind are understood to be multiple and always shifting. Given this more fluid account of the extended mind, we argue that ambient smart environments should be thought of as extended allostatic control systems, operating more or less invisibly to support an agent’s biological capacity for minimising uncertainty over multiple, interlocking timescales. Thus, we account for the functionality of ambient smart environments as extended systems, and in so doing, utilise a markedly different version of the classical thesis of extended mind.

Conscious states—state that there is something it is like to be in—seem both rich or full of detail and ineffable or hard to fully describe or recall. The problem of ineffability, in particular, is a longstanding issue in philosophy that partly motivates the explanatory gap: the belief that consciousness cannot be reduced to underlying physical processes. Here, we provide an information theoretic dynamical systems perspective on the richness and ineffability of consciousness. In our framework, the richness of conscious experience corresponds to the amount of information in a conscious state and ineffability corresponds to the amount of information lost at different stages of processing. We describe how attractor dynamics in working memory would induce impoverished recollections of our original experiences, how the discrete symbolic nature of language is insufficient for describing the rich and high-dimensional structure of experiences, and how similarity in the cognitive function of two individuals relates to improved communicability of their experiences to each other. While our model may not settle all questions relating to the explanatory gap, it makes progress toward a fully physicalist explanation of the richness and ineffability of conscious experience—two important aspects that seem to be part of what makes qualitative character so puzzling.