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An Adaptive Conceptualisation of Artificial Intelligence and the Law, Regulation and Ethics

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  • Ikpenmosa Uhumuavbi

    (Central Asian Legal Research Fellow, Tashkent State University of Law, Sayilgokh Street 35, Tashkent 100047, Uzbekistan)

Abstract

The description of a combination of technologies as ‘artificial intelligence’ (AI) is misleading. To ascribe intelligence to a statistical model without human attribution points towards an attempt at shifting legal, social, and ethical responsibilities to machines. This paper exposes the deeply flawed characterisation of AI and the unearned assumptions that are central to its current definition, characterisation, and efforts at controlling it. The contradictions in the framing of AI have been the bane of the incapacity to regulate it. A revival of applied definitional framing of AI across disciplines have produced a plethora of conceptions and inconclusiveness. Therefore, the research advances this position with two fundamental and interrelated arguments. First, the difficulty in regulating AI is tied to it characterisation as artificial intelligence. This has triggered existing and new conflicting notions of the meaning of ‘artificial’ and ‘intelligence’, which are broad and largely unsettled. Second, difficulties in developing a global consensus on responsible AI stem from this inconclusiveness. To advance these arguments, this paper utilises functional contextualism to analyse the fundamental nature and architecture of artificial intelligence and human intelligence. There is a need to establish a test for ‘artificial intelligence’ in order ensure appropriate allocation of rights, duties, and responsibilities. Therefore, this research proposes, develops, and recommends an adaptive three-elements, three-step threshold for achieving responsible artificial intelligence.

Suggested Citation

  • Ikpenmosa Uhumuavbi, 2025. "An Adaptive Conceptualisation of Artificial Intelligence and the Law, Regulation and Ethics," Laws, MDPI, vol. 14(2), pages 1-28, March.
    • Handle: RePEc:gam:jlawss:v:14:y:2025:i:2:p:19-:d:1615370
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    References listed on IDEAS

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    1. Collins, Christopher & Dennehy, Denis & Conboy, Kieran & Mikalef, Patrick, 2021. "Artificial intelligence in information systems research: A systematic literature review and research agenda," International Journal of Information Management, Elsevier, vol. 60(C).
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