The Philosophy of Artificial Intelligence is an interdisciplinary specialization within the Philosophy of Science, directly related to the foundations of the Natural and Cognitive Sciences. Its formal object addresses central questions concerning the definition, limits, and implications of the set of techniques within Computer Science that emulate results of operations in humans referred to as perception and knowledge—capacities commonly associated with intelligence. The Philosophy of Artificial Intelligence seeks to formulate precise questions that emerge from the invention of AI: whether machines can think, learn, or possess consciousness. It also aims to provide the most well-articulated answers in terms consistent with current science and philosophy.
The roots of this discipline go back to the 1940s and 1950s, with the early developments in cybernetics and computational theory by Norbert Wiener and Alan Turing. Wiener raised ethical concerns about the development of autonomous systems, anticipating issues related to the social impact of automation and AI. Meanwhile, Turing placed at the center of the debate the very possibility of considering the internal processes of certain advanced machines as forms of intelligence or thought. These early proposals marked the beginning of the philosophical debate on the capacity of machines to emulate cognitive processes.
During the 1960s and 1970s, the field of AI advanced rapidly in technical terms, which simultaneously fueled philosophical reflections on the subject. The first epistemological arguments of note emerged from the works of Michael Polanyi, Mortimer Taube, and Mario Bunge, who—from different standpoints—highlighted the difficulty of capturing the qualitative dimension of knowledge within computational formalization. Joseph Weizenbaum was among the first mathematicians and AI engineers to urge his peers to consider the ethical implications of the proliferation of AI. Stanley Jaki and John Lucas deepened the epistemological exploration of the potential functional scope of computers, based on arguments emphasizing the distinction between brain and mind. Analytical philosophers such as John Searle, Hilary Putnam, and Hubert Dreyfus also challenged some of the assumptions behind so-called “symbolic” AI, setting theoretical limits to its capabilities. Other philosophers took a more optimistic view, such as Margaret Boden and Daniel Dennett, suggesting that consciousness and mind could result from complex computational processes—an outlook that aligns AI with functionalist theories of mind. Along this line of thought stand major precursors of the idea of superintelligent AI, like Marvin Minsky, and proponents of digital supremacy such as Hans Moravec and Ray Kurzweil.
In the final decades of the 20th century, the discipline expanded with the emergence of new AI techniques, such as neural networks and expert systems, which reignited questions around intentionality, autonomy, and the ethics of algorithms, particularly in the work of thinkers like Hubert Dreyfus. It is also important to acknowledge the contributions of philosophers such as Gilbert Simondon and psychologists like Sherry Turkle, who, while not focused exclusively on AI, offer profound analyses of technology and its relationship with individuals and society—insights that apply directly to a Philosophy of AI.
Today, the Philosophy of AI addresses not only ontological questions regarding the nature of artificial intelligence but also ethical, political, and social issues. The rise of large language models and generative AI systems has brought to the forefront concerns about responsibility, algorithmic bias, and the autonomy of such systems. Contemporary philosophers like Nick Bostrom explore the existential risks associated with superintelligent singularity, while others, like Luciano Floridi, develop ethical frameworks to guide the development of technologies that respect human values. In this sense, the Philosophy of Artificial Intelligence has evolved—not only encompassing theoretical debates about AI’s possibilities, but also becoming a critical discipline that, in dialogue with developers, influences the design, regulation, and application of technologies that profoundly reshape our society.
¿Popularization narrative or scientific interdisciplinarity?
The current AI landscape is as promising as it is complex. Major advances in development and application provide concrete benefits, but also carry with them a number of social challenges to be dealt with. In addition to this, old debates, once exclusively of interest to academics and experts, have come into the public sphere, spawning adherents of all kinds. Without diminishing their rank, these debates can be conceptually sorted into one of figure and one of ground. The first one is the argument –taking place inside academia and among developers, but closely followed by markets and governments– over whether AIs have, will have or could have true intelligence, whether this can be “general” in scope and “strong” in capacity, and what impact would each of these options have on society at large. The second one is the ongoing discussion in cognitive science, neuroscience, and other disciplines interested in the mind, language, knowledge and consciousness, where deep consensuses among disciplines and experts are yet to be reached.
In this context, the popularization narratives that flood the mass media, courtesy of the pen or image of opportunistic pundits and futurologists of the hour, are of little help. Both species concur in the pragmatics of transforming possible hypotheses into firm and proven statements, ignoring unresolved technical aspects and betting on either an optimistic dystopia or a pessimistic paroxysm, depending on what best fits their discourse.
Thus, to speak of a “philosophy of AI” may invite some prejudice. In a hasty interpretation, it could be argued that a philosophical approach to artificial intelligences would merely add another voice to the media rhetoric. Or, granting it a better discernment, it could be seen as part of the underlying multidisciplinary discussion, but one that only contributes to deepening the problems rather than providing solutions.
However, such a belief or view misses an important part of the present reality. In these times in which, thanks to AIs themselves, data and processes are so accessible and ubiquitous, the capacity for intellectual elaboration and critical thinking –which, not coincidentally, are pillars of philosophical practice– are increasingly valued in the most varied fields.
Furthermore, the epistemological and anthropological framework provided by a philosophy of AI has the potential to close rifts or, at least, produce rapprochements. On the one hand, between actors from different disciplines that are involved value chain of AI development, providing them with a context for interdisciplinary integration. On the other hand, between the technology itself and its users, offering ethical criteria for innovation and collaborative adoption.
Philosophy of Artificial Intelligence
Karl Popper argues that all science begins with philosophical problems and ends with philosophical problems. In the field of Computer Science, the disciplines grouped under the denomination of “artificial intelligence” (AI) do not seem to be an exception. A cursory glance at the seminal writings of the founders and the developments of pioneers in the field reveals that their original concerns are of a philosophical and epistemological order. In a second stage, the formulation of derived problems in conjunction with the search for scientific answers establishes, more or less organically, some substantive theories of AIs. This somewhat fragmentary theoretical framework –and still lacking in general consensuses– constitutes, together with the evolving operational theories, the technical scaffolding that supports AIs as formal objects and technological artifacts.