Critiques of Artificial Reason

Artificial Intelligence, 1989

Portions of this document may be illegible electronic image products. Images are produced from the best available original document.

Minds and Machines, 2025

This paper argues that explainability is only one facet of a broader ideal that shapes our expectations towards artificial intelligence (AI). Fundamentally, the issue is to what extent AI exhibits systematicity-not merely in being sensitive to how thoughts are composed of recombinable constituents, but in striving towards an integrated body of thought that is consistent, coherent, comprehensive, and parsimoniously principled. This richer conception of systematicity has been obscured by the long shadow of the "systematicity challenge" to connectionism, according to which network architectures are fundamentally at odds with what Fodor and colleagues termed "the systematicity of thought." I offer a conceptual framework for thinking about "the systematicity of thought" that distinguishes four senses of the phrase. I use these distinctions to defuse the perceived tension between systematicity and connectionism and show that the conception of systematicity that historically shaped our sense of what makes thought rational, authoritative, and scientific is more demanding than the Fodorian notion. To determine whether we have reason to hold AI models to this ideal of systematicity, I then argue, we must look to the rationales for systematization and explore to what extent they transfer to AI models. I identify five such rationales and apply them to AI. This brings into view the "hard systematicity challenge." However, the demand for systematization itself needs to be regulated by the rationales for systematization. This yields a dynamic understanding of the need to systematize thought, which tells us how systematic we need AI models to be and when.

Synthese 170: 131-146, 2009

In this paper I critically examine the notion of explanation used in artificial intelligence in general, and in the theory of belief revision in particular. I focus on two of the best known accounts in the literature: Pagnucco’s abductive expansion functions and Gärdenfors’ counterfactual analysis. I argue that both accounts are at odds with the way in which this notion has historically been understood in philosophy. They are also at odds with the explanatory strategies used in actual scientific practice. At the end of the paper I outline a set of desiderata for an epistemologically motivated, scientifically informed belief revision model for explanation.

Minds and Machines, 1999

and Human Reason: A Teleological Critique, New York: Columbia University Press, 1991, xii + 209 pp., $35.00 (cloth), ISBN 0-231-07290-2.

2004

Donald Gillies is one of the pioneers in the philosophical analysis of artificial intelligence (AI). In his recent book, Gillies (1996) not only makes a new and rapidly developing field of science accessible to philosophers; he also introduces philosophical topics relevant to researchers in AI and thereby helps establish a dialogue between the two disciplines. His book clearly and convincingly demonstrates the fruitful interplay between AI and philosophy of science.

Science and Engineering Ethics, 2019

This paper discusses the problem of responsibility attribution raised by the use of artificial intelligence (AI) technologies. It is assumed that only humans can be responsible agents; yet this alone already raises many issues, which are discussed starting from two Aristotelian conditions for responsibility. Next to the well-known problem of many hands, the issue of “many things” is identified and the temporal dimension is emphasized when it comes to the control condition. Special attention is given to the epistemic condition, which draws attention to the issues of transparency and explainability. In contrast to standard discussions, however, it is then argued that this knowledge problem regarding agents of responsibility is linked to the other side of the responsibility relation: the addressees or “patients” of responsibility, who may demand reasons for actions and decisions made by using AI. Inspired by a relational approach, responsibility as answerability thus offers an important additional, if not primary, justification for explainability based, not on agency, but on patiency.

This paper presents a unifying framework for the study of artificial life, intelligence and reality. By providing this framework we can give a clear and concise introduction to the fundamental arguments of all three artificial sciences and facilitate the translation of arguments from any one domain to the other two. The framework is based on a variant of functionalism that does not exclude the role of the observer.

Artificial Intelligence: Critical Concepts, 2000

General Introduction to the four-volume reference work "Artificial Intelliegnce: Critical Concepts"

Minds and Machines, 2003

A situated agent is one which operates within an environment. In most cases, the environment in which the agent exists will be more complex than the agent itself. This means that an agent, human or artificial, which wishes to carry out non-trivial operations in its environment must use techniques which allow an unbounded world to be represented within a cognitively bounded agent. We present a brief description of some important theories within the fields of epistemology and metaphysics. We then discuss ways in which philosophical problems of scepticism are related to the problems faced by knowledge representation. We suggest that some of the methods that philosophers have developed to address the problems of epistemology may be relevant to the problems of representing knowledge within artificial agents.

In this research paper, I argue that, with the new paradigm of deep learning, we should further incorporate evaluation, accountability and acceptance within the interface human-agent. As we shall see, this leads us to move from explainability to explanation.