Understanding human behaviour, neuroscience and psychology using the concepts of ‘computer’, ‘software and hardware’ and ‘AI’ is becoming increasingly popular. In popular media and parlance, people speak of being ‘overloaded’ like a CPU, ‘computing an answer to a question’, of ‘being programmed’ to do something. Now, given the massive integration of AI technologies into our daily lives, AI-related concepts are being used to metaphorically compare AI systems with human behaviour and/or cognitive abilities like language acquisition. Rightfully, the epistemic success of (...) these metaphorical comparisons should be debated. Against the backdrop of the conflicting positions of the ‘computational’ and ‘meat’ chauvinisms, we ask: can the conceptual constellation of the computational and AI be applied to the human domain and what does it mean to do so? What is one doing when the conceptual constellations of AI in particular are used in this fashion? Rooted in a Wittgensteinian view of concepts and language-use, we consider two possible answers and pit them against each other: either these examples are conceptual metaphors, or they are attempts at conceptual engineering. We argue that they are conceptual metaphors, but that (1) this position is unaware of its own epistemological contingency, and (2) it risks committing the “map-territory fallacy”. Down at the conceptual foundations of computation, (3) it most importantly is a misleading ‘double metaphor’ because of the metaphorical connection between human psychology and computation. In response to the shortcomings of this projected conceptual organisation of AI onto the human domain, we argue that there is a semantic catch. The perspective of the conceptual metaphors shows avenues for forms of conceptual engineering. If this methodology’s criteria are met, the fallacies and epistemic shortcomings related to the conceptual metaphor view can be bypassed. At its best, the cross-pollution of the human and AI conceptual domains is one that prompts us to reflect anew on how the boundaries of our current concepts serve us and how they could be approved. (shrink)

The development and deployment of machine learning and artificial intelligence (AI) engender “AI colonialism”, a term that conceptually overlaps with “data colonialism”, as a form of injustice. AI colonialism is in need of decolonization for three reasons. Politically, because it enforces digital capitalism’s hegemony. Ecologically, as it negatively impacts the environment and intensifies the extraction of natural resources and consumption of energy. Epistemically, since the social systems within which AI is embedded reinforce Western universalism by imposing Western colonial values on (...) the global South when these manifest in the digital realm is a form of digital capitalism. These reasons require a new conceptualization of AI decolonization. First this paper draws from the historical debates on the concepts of colonialism and decolonization. Secondly it retrieves Achille Mbembe’s notion of decolonization as disenclosure to argue that the decolonization of AI will have to be the abolishment of political, ecological and epistemic borders erected and reinforced in the phases of its design, production, development of AI in the West and drawing from the knowledge from the global South. In conclusion, it is discussed how conceiving of decolonial AI as form of disenclosure opens up new ways to think about and intervene in colonial instantiations of AI development and deployment, in order to empower “the wretched of AI”, re-ecologise the unsustainable ecologies AI depends on and to counter the colonial power structures unreflective AI deployment risks to reinforce. (shrink)

As the geopolitical superpowers race to regulate the digital realm, their divergent rights-centered, market-driven, and social-control-based approaches require a global compact on digital regulation. If diverse regulatory jurisdictions remain, forms of domination entailed by cultural imposition and hermeneutical injustice related to AI legislation and AI systems will follow. We argue for consensual regulation on shared substantive issues, accompanied by proper standardization and coordination. Failure to attain consensus will fragment global digital regulation, enable regulatory capture by authoritarian powers or bad corporate (...) actors, and deepen the historical geopolitical power asymmetries between the global South and the global North. To prevent an unjust regulatory landscape where the global South’s cultural and hermeneutic resources are absent, two principles for the Global Digital Compact to counter these prospective harms are proposed and discussed: (1) “recognitive consensus on key substantive benefits and harms” and (2) “procedural consensus on global coordination and essential standards.”. (shrink)

Is it sensical to ascribe psychological predicates to AI systems like chatbots based on large language models (LLMs)? People have intuitively started ascribing emotions or consciousness to social AI (‘affective artificial agents’), with consequences that range from love to suicide. The philosophical question of whether such ascriptions are warranted is thus very relevant. This paper advances the argument that LLMs instantiate language users in Ludwig Wittgenstein’s sense but that ascribing psychological predicates to these systems remains a functionalist temptation. Social AIs (...) are not full-blown language users, but rather more like Italo Calvino’s literature machines. The ideas of LLMs as Wittgensteinian language users and Calvino’s literature-producing writing machine are combined. This sheds light on the misguided functionalist temptation inherent in moving from equating the two to the ascription of psychological predicates to social AI. Finally, the framework of mortal computation is used to show that social AIs lack the basic autopoiesis needed for narrative façons de parler and their role in the sensemaking of human (inter)action. Such psychological predicate ascriptions could make sense: the transition ‘from quantity to quality’ can take place, but its route lies somewhere between life and death, not between affective artifacts and emotion approximation by literature machines. (shrink)

Whether related to machine learning models’ epistemic opacity, algorithmic classification systems’ discriminatory automation of testimonial prejudice, the distortion of human beliefs via the hallucinations of generative AI, the inclusion of the global South in global AI governance, the execution of bureaucratic violence via algorithmic systems, or located in the interaction with conversational artificial agents epistemic injustice related to AI is a growing concern. Based on a proposed general taxonomy of epistemic injustice, this paper first sketches a taxonomy of the types (...) of epistemic injustice in the context of AI, relying on the work of scholars from the fields of philosophy of technology, political philosophy and social epistemology. Secondly, an additional perspective on epistemic injustice in the context of AI: generative hermeneutical erasure. I argue that this injustice that can come about through the application of Large Language Models (LLMs) and contend that generative AI, when being deployed outside of its Western space of conception, can have effects of conceptual erasure, particularly in the epistemic domain, followed by forms of conceptual disruption caused by a mismatch between AI system and the interlocutor in terms of conceptual frameworks. AI systems’ ‘view from nowhere’ epistemically inferiorizes non-Western epistemologies and thereby contributes to the erosion of their epistemic particulars, gradually contributing to hermeneutical erasure. This work’s relevance lies in proposal of a taxonomy that allows epistemic injustices to be mapped in the AI domain and the proposal of a novel form of AI-related epistemic injustice. (shrink)

In Philosophy and Technology 36, David Watson discusses the epistemological and metaphysical implications of unsupervised machine learning (ML) algorithms. Watson is sympathetic to the epistemological comparison of unsupervised clustering, abstraction and generative algorithms to human cognition and sceptical about ML’s mechanisms having ontological implications. His epistemological commitments are that we learn to identify “natural kinds through clustering algorithms”, “essential properties via abstraction algorithms”, and “unrealized possibilities via generative models” “or something very much like them.” The same issue contains a commentary (...) on Watson’s paper in which Tom Sterkenburg fiercely opposes the epistemological claim that clustering algorithms can identify natural kinds. Sterkenburg argues there’s nothing about clustering arguments themselves that enables natural kind identification. What’s at stake in the Watson-Sterkenburg debate is whether it is possible for clustering algorithms to identify natural kinds and whether they underly human identification of natural kinds. My contribution to the Watson-Sterkenburg debate is twofold. First, I argue Sterkenburg’s criticism of Watson’s claim is too severe because it denies any clustering algorithm to identify any natural kind. Secondly, I argue Watson’s reply overestimates both clustering algorithms’ and humans’ access to natural kinds and his claim has to be restricted accordingly to ‘We identify kinds via clustering algorithms, or something very much like them’. (shrink)