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Scatter plots of different hypothesis tests for opinions on definitions of machine (plots on the left) and human intelligence (plots on the right).
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There are several reasons for the lack of a consensus definition of (machine) intelligence. The constantly evolving nature and the interdisciplinarity of the Artificial Intelligence (AI) field, together with a historical polarization around what intelligence means, are among the most widely discussed rationalizations, both within the community and...
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Citations
... Much of the confusion around what AI stems from the lack of consensus on its definition (Monett et al. 2019). It has been a very controversial and contextual concept over decades, part of a "conceptual borrowing" of AI as a discipline (Floridi and Nobre, 2024). ...
We are experiencing a massive and volatile expansion of AI-based products and services. The current intermeshing of digital technologies, people, and society is shaping how we live and bringing algorithms to the forefront of decision making. The algorithmification of society and the narratives used to make it appear inevitable serve specific interests, mostly profitable for and controlled by few actors. It is not AI in itself, but the utilitarian sophistication of optimisation mechanisms and the power structures behind them that profit from controlling all that we do, when and how we do it, our behaviours, and even ourselves. In education, this is of serious concern as academia is gradually moving to uncertain dependencies on corporate interests. This paper calls for radical changes in dealing with the AI narratives that have monopolised recent public debates and discussions. It sheds light on the key terminology surrounding today’s AI algorithms and the technological background that makes them possible. It shows examples of the negative impacts and the implications of not addressing or ignoring certain issues, especially in education. This paper also suggests good practices through consistent advocacy, grounded materials, and critical work on digital literacy, particularly AI literacy.
... Much of the confusion around what AI stems from the lack of consensus on its definition (Monett et al. 2019). It has been a very controversial and contextual concept over decades, part of a "conceptual borrowing" of AI as a discipline (Floridi and Nobre, 2024). ...
We are experiencing a massive and volatile expansion of AI-based products and services. The current intermeshing of digital technologies, people, and society is shaping how we live and bringing algorithms to the forefront of decision making. The algorithmification of society and the narratives used to make it appear inevitable serve specific interests, mostly profitable for and controlled by few actors. It is not AI in itself, but the utilitarian sophistication of optimisation mechanisms and the power structures behind them that profit from controlling all that we do, when and how we do it, our behaviours, and even ourselves. In education, this is of serious concern as academia is gradually moving to uncertain dependencies on corporate interests. This paper calls for radical changes in dealing with the AI narratives that have monopolised recent public debates and discussions. It sheds light on the key terminology surrounding today's AI algorithms and the technological background that makes them possible. It shows examples of the negative impacts and the implications of not addressing or ignoring certain issues, especially in education. This paper also suggests good practices through consistent advocacy, grounded materials, and critical work on digital literacy, particularly AI literacy.
... The lack of consensus on the definition of AI is expected and is caused by different cognitive biases [12]. These biases form part of people's judgment and cannot always be avoided. ...
With the explosion of Artificial Intelligence (AI) as an area of study and practice, it has gradually become very difficult to mark its boundaries precisely and specify what exactly it encompasses. Many other areas of study are interwoven with AI, and new research and development topics that require interdisciplinary approach frequently attract attention. In addition, several AI subfields and topics are home to long-time controversies that give rise to seemingly never-ending debates that further obfuscate the entire area of AI and make its boundaries even more indistinct. To tackle such problems in a systematic way, this paper introduces the concept of identity of AI (viewed as an area of study) and discusses its dynamics, controversies, contradictions, and opposing opinions and approaches, coming from different sources and stakeholders. The concept of identity of AI emerges as a set of characteristics that shape up the current outlook on AI from epistemological, philosophical, ethical, technological, and social perspectives.
... There are several underlying reasons for disagreement on defining intelligence whose analysis would be beyond the scope of this paper (we refer the interested reader to [7] and [8] for related discussions on the lack of consensus). In Hunt and Jaeggi's [7] words, "[i]t is not surprising that defining the subject matter of intelligence research has been difficult, for in everyday discourse the word intelligence is used in various ways." ...
Delineating the boundaries of the discourse on machine or artificial intelligence (AI) may help in defining and understanding its most discussed concept, the concept of intelligence. Furthermore, better insights into both definitions and how to define them well has proven to be essential for a better understanding of concepts, intelligence included.
These and related cognitive abilities (e.g. defining, analyzing, understanding, discussing, and comparing definitions of intelligence, among others) are expected for AI researchers and practitioners in the first place. Yet, they are also central to extending or at least providing the basics of AI literacy to other stakeholders of our society. Intelligent systems are transforming the way we interact with technology, with each other, and with ourselves, and knowing at least what AI or intelligence mean is becoming essential for designing, developing, deploying, using, and even regulating intelligent artefacts.
However, defining intelligence has been one of the most controversial and studied challenges of both ancient and modern human thinking. A lack of consensus on what intelligence is has remained almost constant over the centuries. Interested scholars have not come up with a consensus or cross-domain accepted definition of intelligence. Neither in the ancient Eastern nor in the ancient and contemporary Western conceptions of intelligence. Nor in the more recent perspectives from the last 70 years within the field of AI.
We argue that a better understanding of contemporary technologies, AI-based but not only, starts with a grounded exposure to their conceptual pillars. These include fundamental concepts like the concept of intelligence, in general, and of AI, in particular. Learners and decision makers at all levels should face them, as well as be able to discuss their importance and limitations critically and in an informed way. For doing that, they must be confronted with definitions of intelligence and understand their meaning well, for instance. If these contents are already part of study programs, the better.
It is the main goal of this paper to present how a few hundreds of definitions of intelligence were annotated, i.e. their properties and characteristics systematically analyzed and commented, in order to construct a corpus (i.e. a collection) of definitions of intelligence for further uses in AI and other fields. The work and particular application domain presented here has not yet been considered in the extended work on linguistic annotation. Even though, both the annotation and the data merit special attention, for they deal with the elusive, important concept of intelligence, i.e. with definitions of both human and machine (or artificial) intelligence.
Undergraduate Computer Science students carried out the annotation process and several research activities. They were involved in an AI research project led by faculty and included their findings and work as part of their undergraduate student research projects in their last study year. We provide details about how the student research projects were conceived, conducted, and mentored. We also describe the properties or quality criteria that were considered for annotating the definitions from the intelligence corpus.
... With this two-part paper we intend to give answers to these and other related questions and to help bridge diverse research fields on defining definitions. Along with previous work that analyzes cognitive biases in experts' written opinions about definitions of intelligence [16], we also seek to understand why finding a "concordant" definition might be difficult to accomplish although not impossible. Paraphrasing Kate Raworth's approach [21] when she calls for rethinking economics as a science, defining machine intelligence is not about finding the perfect definition, 2 because it does not exist yet and maybe will not; it's about establishing a definition that reflects the context we face, the values we hold, and the aims we have in the field. ...
We posit that the lack of consensus definitions of (machine or artificial) intelligence might be affected by the lack of knowledge of conceptual analysis and other well-investigated theories. Acute contextualization of the concepts that are defined may also be an issue. Accordingly, in this two-part paper, we review some basic concepts from across research fields on how to explicate a definition. In Part I we propose 30 quality criteria for definitions that shall serve as guidelines for well-defined definitions of any concept. The quality criteria may allow for both better insights into definitions and a wider understanding of the current discourse on AI. In Part II we provide basic terminology on definitions and an iterative process to guide the construction of robust definitions by considering the quality criteria introduced in Part I. Our central goal is twofold: we want to facilitate understanding across fields and inform different stakeholders from industry, academia, legal and governments, among others, by contributing to the formal foundations on elucidating "good and robust definitions" for AI.
