Artificial Intelligence: Panacea or Non-intentional Dehumanisation?

van der Gun, Luc; Guest, Olivia

doi:10.31235/osf.io/rh4fw

Cited by 3 publications

(2 citation statements)

References 22 publications

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“…We also acknowledge existing critiques of 'AI as computationalism'. Some of those critiques may target makeism (and/or dehumanisation; Baria & Cross, 2021;Birhane, 2021;Birhane & van Dijk, 2020;Erscoi et al, 2023;van der Gun & Guest, 2023) more than computationalism as a theoretical tool per se (but we will leave that judgement up to the critics). Be that as it may, we believe it is useful to explain how makeism and computationalism are dissociable, just as cognitivism and computationalism are dissociable (M. Villalobos & Dewhurst, 2017 and representationalism and computationalism are dissociable (Miłkowski, 2013(Miłkowski, , 2018Piccinini, 2008).…”

Section: Theory Without Makeingmentioning

confidence: 99%

“…Besides the various psychological, social, cultural and political problems posed by this confusion (Bender, Gebru, McMillan-Major, & Shmitchell, 2021;Birhane & van Dijk, 2020;Erscoi et al, 2023;J. Hughes, 2021;Larson, 2021;Thrall et al, 2018;Vallor, 2015;van der Gun & Guest, 2023;Wood, 1987), here we wish to focus on how this practice creates distorted and impoverished views of ourselves and deteriorates our theoretical understanding of cognition, rather than advancing and enhancing it.…”

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confidence: 99%

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Reclaiming AI as a theoretical tool for cognitive science

Rooij¹,

Guest²,

Adolfi³

et al. 2023

Preprint

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The idea that human cognition is, or can be understood as, a form of computation is a useful conceptual tool for cognitive science. It was a foundational assumption during the birth of cognitive science as a multidisciplinary field, with Artificial Intelligence (AI) as one of its contributing fields. One conception of AI in this context is as a provider of computational tools (frameworks, concepts, formalisms, models, proofs, simulations, etc.) that support theory building in cognitive science. The contemporary field of AI, however, has taken the theoretical possibility of explaining human cognition as a form of computation to imply the practical feasibility of realising human(-like or -level) cognition in factual computational systems; and, the field frames this realisation as a short-term inevitability. Yet, as we formally prove herein, creating systems with human(-like or -level) cognition is intrinsically computationally intractable. This means that any factual AI systems created in the short-run are at best decoys. When we think these systems capture something deep about ourselves and our thinking, we induce distorted and impoverished images of ourselves and our cognition. In other words, AI in current practice is deteriorating our theoretical understanding of cognition rather than advancing and enhancing it. The situation could be remediated by releasing the grip of the currently dominant view on AI and by returning to the idea of AI as a theoretical tool for cognitive science. In reclaiming this older idea of AI, however, it is important not to repeat conceptual mistakes of the past (and present) that brought us to where we are today.

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Section: Theory Without Makeingmentioning

confidence: 99%

mentioning

confidence: 99%

Reclaiming AI as a theoretical tool for cognitive science

Rooij¹,

Guest²,

Adolfi³

et al. 2023

Preprint

View full text Add to dashboard Cite

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What Makes a Good Theory, and How Do We Make a Theory Good?

Guest

2024

Comput Brain Behav

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I present an ontology of criteria for evaluating theory to answer the titular question from the perspective of a scientist practitioner. Set inside a formal account of our adjudication over theories, a metatheoretical calculus, this ontology comprises the following: (a) metaphysical commitment, the need to highlight what parts of theory are not under investigation, but are assumed, asserted, or essential; (b) discursive survival, the ability to be understood by interested non-bad actors, to withstand scrutiny within the intended (sub)field(s), and to negotiate the dialectical landscape thereof; (c) empirical interface, the potential to explicate the relationship between theory and observation, i.e., how observations relate to, and affect, theory and vice versa; (d) minimising harm, the reckoning with how theory is forged in a fire of historical, if not ongoing, abuses—from past crimes against humanity, to current exploitation, turbocharged or hyped by machine learning, to historical and present internal academic marginalisation. This work hopes to serve as a possible beginning for scientists who want to examine the properties and characteristics of theories, to propose additional virtues and vices, and to engage in further dialogue. Finally, I appeal to practitioners to iterate frequently over such criteria, by building and sharing the metatheoretical calculi used to adjudicate over theories.

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Reclaiming AI as a Theoretical Tool for Cognitive Science

van Rooij,

Guest,

Adolfi

et al. 2024

Comput Brain Behav

View full text Add to dashboard Cite

The idea that human cognition is, or can be understood as, a form of computation is a useful conceptual tool for cognitive science. It was a foundational assumption during the birth of cognitive science as a multidisciplinary field, with Artificial Intelligence (AI) as one of its contributing fields. One conception of AI in this context is as a provider of computational tools (frameworks, concepts, formalisms, models, proofs, simulations, etc.) that support theory building in cognitive science. The contemporary field of AI, however, has taken the theoretical possibility of explaining human cognition as a form of computation to imply the practical feasibility of realising human(-like or -level) cognition in factual computational systems, and the field frames this realisation as a short-term inevitability. Yet, as we formally prove herein, creating systems with human(-like or -level) cognition is intrinsically computationally intractable. This means that any factual AI systems created in the short-run are at best decoys. When we think these systems capture something deep about ourselves and our thinking, we induce distorted and impoverished images of ourselves and our cognition. In other words, AI in current practice is deteriorating our theoretical understanding of cognition rather than advancing and enhancing it. The situation could be remediated by releasing the grip of the currently dominant view on AI and by returning to the idea of AI as a theoretical tool for cognitive science. In reclaiming this older idea of AI, however, it is important not to repeat conceptual mistakes of the past (and present) that brought us to where we are today.

show abstract

Artificial Intelligence: Panacea or Non-intentional Dehumanisation?

Cited by 3 publications

References 22 publications

Reclaiming AI as a theoretical tool for cognitive science

Reclaiming AI as a theoretical tool for cognitive science

What Makes a Good Theory, and How Do We Make a Theory Good?

Reclaiming AI as a Theoretical Tool for Cognitive Science

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