Challenges for an Ontology of Artificial Intelligence

OSF Preprint, 2025

We must reconsider our relationship with machines as artificial intelligence evolves from task-based support to autonomous or assisted generation. Generation is not creation. Human intelligence–our only known model –is the sole benchmark for evaluating AI. Yet, we lack rigorous comparison standards because we do not fully understand the internal mechanisms of human intelligence. Perhaps it is more appropriate to speak of AI in simplified terms–as something that, under certain conditions, emulates what we commonly perceive as intelligent behavior. But even that remains uncertain. To call a circuit board “intelligent,” in my view, is merely an exercise in measuring how well emulation mimics what we recognize as intelligence, based solely on the only model we know: our own. Let us not forget that we still lack a universally accepted definition of intelligence, let alone a definition of thinking, or even a deeper understanding of the complex nature of consciousness. Let us also remember that over the last 80 years of computing and automation, the dominant discipline for solving problems has not been AI, but engineering. Engineering provides the methodologies, mental models, and validation frameworks we use to design and deploy systems. AI extends this legacy — and at the same time, disrupts it, introducing new epistemic and ethical challenges that cannot be resolved solely through efficiency. This manifesto examines why and how AI must evolve in tandem with human cognition, roles, and ethics. It argues that AI demands more than technical or economic scrutiny—it calls for a philosophical revolution in understanding intelligence, responsibility, and human-machine interaction. At the heart of this transition lies the need to articulate a new intellectual discipline: the Philosophy of the Machines. In a world increasingly divided between the celebration of individual agency amplified by AI tools and the calls for stricter regulatory frameworks to govern “machine autonomy”, the Philosophy of the Machines proposes a third way: a foundational philosophical inquiry into how machines reshape the conditions of thinking, acting, and ethical responsibility itself. This is not a technical manual, nor a speculative vision. It is a conceptual framework designed to reframe the logic of AI adoption, shifting from defining efficiency, value, and oversight to allocating agency in automated systems. The Δ–η–ζ model introduced here is not a turnkey tool but a thinking scaffold — a way to build more realistic, ethical, and human-aligned business cases for AI and GenAI. This manifesto aims to open the debate: • What kind of intelligence are we building? • What kind of humans must we become in response? • And how do we share responsibility with systems we no longer fully control? This manifesto is neither techno-optimistic nor purely regulatory; it is ontological, epistemological, and systemic, offering a new intellectual framework for the Age of Artificial Agents. This manifesto invites reflection on these questions not as an academic exercise, but as a call to reshape the human condition in the age of artificial agents.

This chapter offers a critical perspective on the contingent formation of artificial intelligence as a key sociotechnical institution in contemporary societies. It shows how the development of AI is not merely a product of functional technological development and improvement but depends just as much on economical, political, and discursive drivers. It builds on work from STS and critical algorithm studies surfacing that technological developments are always contingent on and resulting from transformations along multiple scientific trajectories as well as interaction between multiple actors and discourses. For our conceptual understanding of AI and its epistemology, this is a consequential perspective. It directs attention on different issues: away from detecting impact and bias ex post, and towards a perspective that centers on how AI is coming into being as a powerful sociotechnical entity. We illustrate this process in three key domains: technological research, media discourse, an...

Human-Machine Communication, 2020

In human-machine communication, people interact with a communication partner that is of a different ontological nature from themselves. This study examines how people conceptualize ontological differences between humans and computers and the implications of these differences for human-machine communication. Findings based on data from qualitative interviews with 73 U.S. adults regarding disembodied artificial intelligence (AI) technologies (voice-based AI assistants, automated-writing software) show that people differentiate between humans and computers based on origin of being, degree of autonomy, status as tool/tool-user, level of intelligence, emotional capabilities, and inherent flaws. In addition, these ontological boundaries are becoming increasingly blurred as technologies emulate more human-like qualities, such as emotion. This study also demonstrates how people’s conceptualizations of the human-computer divide inform aspects of their interactions with communicative technolo...

Current Journal of Applied Science and Technology, 2023

The recent appearance of widely available Artificial Intelligence (AI) applications such as ChatGPT and Bard has fuelled a flurry of popular and academic discussions about the implications of such AI tools for all aspects of contemporary life and culture. After examining some aspects of recent developments, this article goes on to review some salient critiques of the emerging AI debate with the intention of analyzing some of the key themes in the current discourse. The key objective will be to deflate some of the more overblown and alarming perspectives informed by anthropomorphising AI developments. In particular, there is an attempt to steer a middle course between doom-laden pessimism and futuristic optimism. Drawing on a range of philosophical positions, it is concluded that AI applications are best conceptualised as powerful tools which need to be utilised pragmatically and regulated ethically in partnership with humans in the best interests of all of us.

The article focuses on the different themes of Artificial Intelligence (AI) and its varied impacts on our future. The exploration is guided by the question; how will the thematic dynamics of AI shape our future? To achieve this, the paper provides a historical overview of AI, including its evolution from theoretical beginnings to its present, whereby it is applied in almost all fields. The paper also explores AI’s transformative role in various sectors, including healthcare, finance, manufacturing, and education. The process emphasizes how AI fosters efficiency and innovation. The researcher has also given significant attention to the socio-economic benefits of AI, including improved efficiency, healthcare advancements, and educational accessibility. However, the paper also addresses the darker aspects, including job displacement, ethical concerns, and the risks of AI in warfare and security. The discussion mainly dwells on how AI is dual-edged, a potential enhancer and disruptor of operations in those areas where it applies. The paper provides an in-depth analysis of the key themes of AI, especially autonomy and intelligence augmentation, examining their influences on societal norms. This scrutiny unfolds the complex relationship between technological advancement and human existence by exploring its varied aspects ranging from socio-economic benefits to ethical concerns. The paper, therefore, aims to unravel the intricate tapestry of AI’s role in shaping our future trajectory. An inquiry into the issue at hand provides insights into AI’s potential to revolutionize our world while taking due consideration of its ethical and social challenges. As such, the paper advocates for a balanced approach to AI development, considering its complex relationships with societal norms and ethical standards, highlighting the need for global cooperation in AI governance. The future of AI, as discussed in this paper, is a confluence of remarkable possibilities and significant responsibilities, requiring collective efforts to harness its full potential responsibly. Keywords: Artificial Intelligence, Ethical Considerations, Historical Evolution, Societal Impact, Regulation and Governance, Technological AdvancementAI in Healthcare Prospects.

IEEE Potentials, 2000

Few human endeavors can be viewed both as extremely successful and unsuccessful at the same time. This is typically the case when goals have not been well defined or have been shifting in time. This has certainly been true of Artificial Intelligence (AI). The nature of intelligence has been the object of much thought and speculation throughout the history of philosophy. It is in the nature of philosophy that real headway is sometimes made only when appropriate tools become available. For instance, the nature and behavior of physical objects was a major topic of philosophy. That is until the experimental method and the advent of calculus allowed for the development of Physics. Similarly the computer, coupled with the ability to program (at least in principle) any function, appeared to be the tool that could tackle the notion of intelligence. To suit the tool, the problem of the "nature" of intelligence was soon sidestepped in favor of this notion: If a probing conversation with a computer could not be distinguished from a conversation with a human, then "artificial" intelligence had been achieved. This notion became known as the "Turing test", after the mathematician Alan Turing who proposed it in 1950. This challenge quickly attracted the best computer scientists in a worldwide search for techniques and principles of what soon became known as the field of Artificial Intelligence. The early efforts focused on creating "general problem solvers" like, for instance, the Soar system (Newell, Laird and Rosenbloom) which attempted to solve problems by breaking them down into sub-goals. Conceptually rich and interesting, these early efforts gave rise to a large portion of the field's framework. Key to artificial intelligence, rather than the "number crunching" typical of computers until then, was viewed as the ability to manipulate symbols and make logical inferences. To facilitate these tasks, "AI languages" such as LISP and Prolog were invented and used widely in the field. That this quest never strayed far from rigorous mathematical underpinnings was both its strength and its limitation. Its strength was to open a new fertile area of computer science. Its limitation was that "real world" problems tended to be too complex for the limitations imposed by mathematical rigor and the constraints of logic and symbol manipulation. Therefore, much effort continued to be focused on "toy problems." One idea that emerged and enabled some success with real world problems was the notion that "most" intelligence really resided in knowledge. A phrase attributed to Feigenbaum, one of the pioneers, was "knowledge is the power." With this premise, the problem is shifted from "how do we solve problems" to "how do we represent knowledge." A good knowledge representation scheme could allow one to draw conclusions from given premises. Such schemes took forms such as rules, frames and scripts. It allowed the building of what became known as "expert systems" or "knowledge based systems" (KBS). These types of systems could indeed help in real world problems (the author led a project for the first expert system to aid astronauts in performing some scientific experiments. It was called PI-in-a-Box). The technology that ensued from expert systems gave rise to the first instance of an "Al industry." Consulting "Knowledge Engineers" and products (Shells) could take some of the drudgery out of building these types of systems. The enthusiasm of this time, however, masked an important shift that had been made by this technology: "Real world" solutions were obtained by keeping the system's focus extremely narrow and limited in scope. These systems were, and, to a large extent, remain extremely "fragile." That is, unexpected inputs or straying from the scope of the system could easily result in unexpected and erroneous results. The most difficult aspects of intelligence to incorporate appeared to be understanding a) one's limits of knowledge and b) the, unfortunately, elusive "common sense." The very usefulness and continuing success of these types of systems has also brought to light the fundamental limitation of the behaviorist model of intelligence. This model has difficulty coping with the fact that intelligence seems to reside in the ability to achieve one's expertise and to use it appropriately more than, or certainly in addition to, the expertise itself. Again, this realization shouldn't take away from the continuing improvements and successes in these types of systems. Model Based Reasoning has emerged as a powerful approach to diagnosis, and planning and scheduling systems have had much success as well. The point is that AI, now increasingly called "Symbolic AI," has produced a new branch of computer science. Along with it, powerful tools have been created for knowledge representation, symbol manipulation, searching and optimization. AI is alive and well. However, many opine that its picture of intelligence is too fragmented to represent a satisfactory model of cognition.

Minds & Machines, 2019

Artificial intelligence (AI) has historically been conceptualized in anthropomorphic terms. Some algorithms deploy biomimetic designs in a deliberate attempt to effect a sort of digital isomorphism of the human brain. Others leverage more general learning strategies that happen to coincide with popular theories of cognitive science and social epistemology. In this paper, I challenge the anthropomorphic credentials of the neural network algorithm, whose similarities to human cognition I argue are vastly overstated and narrowly construed. I submit that three alternative supervised learning methods-namely lasso penalties, bagging, and boosting-offer subtler, more interesting analogies to human reasoning as both an individual and a social phenomenon. Despite the temptation to fall back on anthropomorphic tropes when discussing AI, however, I conclude that such rhetoric is at best misleading and at worst downright dangerous. The impulse to humanize algorithms is an obstacle to properly conceptualizing the ethical challenges posed by emerging technologies.

I can no longer ignore nor avoid the discussion surrounding Artificial Intelligence (AI), and so I shall share some of my thoughts, given that I am neither proficient in software and programming, nor a man of progress, so that all my comments are no more than speculations and observations of a reluctant historian. What can be seen through historical perspective is that any technological innovation bears with it a potential to improve human life and for destruction. As far as I can see, the big questions about AI can be divided into seven categories: Ontological, Epistemological, Defensive, Ethical, Psychological, Economical, and Aesthetical.

The Cambridge Handbook of Responsible Artificial Intelligence: Interdisciplinary Perspectives, 2022

In this chapter, the philosopher Christoph Durt elaborates a novel view on AI and its relation to humans. He contends that AI is neither merely a tool, nor an artificial subject, nor necessarily a simulation of human intelligence. These traditional and still prevalent misconceptions of AI have led to grave misunderstandings of the opportunities and dangers of AI. A more comprehensive concept of AI is needed to better understand the possibilities of responsible AI. The chapter shows the roots of the misconceptions in the Turing Test. The author argues that the simplicity of the setup of the Turing Test is deceptive, and that Turing was aware that the text exchanges can develop in much more intricate ways than usually thought. The Turing Test only seemingly avoids difficult philosophical questions by passing on the burden to an evaluator, who is part of the setup, and in fact hides in plain sight his or her decisive contribution. Durt shows that, different from all previous technology, AI processes meaningful aspects of the world as experienced and understood by humans. While this gives the appearance that AI resembles humans, AI’s relation to the lifeworld is unique. Durt delineates a more comprehensive picture according to which AI integrates into the human lifeworld through its interrelations with humans and data.

2018

This paper presents an intriguing debate on artificial intelligence and the human person. Science has grown into an unpredictable parlance to the extent that its breakthroughs now re-make creation to reflect man’s desires. This has led, for example, to the attempt to create machines that could work like more competent persons, such that these machines could be equated with human persons or considered as the human counterpart. By implication, the project of artificial intelligence seeks but one thing, that is, to make a ‘prototype man’ or an ‘artificial man’. For, to grant the concept of artificial intelligence is to affirm, without any prejudice, the concept of artificial man. For this reason, this paper argues that there is nothing like artificial intelligence because it is too much of an exaggeration to think of machines that can do things men can do. It maintains that intelligence cannot be assigned to machines and machines do not possess intelligence. Rather, machines merely per...