Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
We’re only human after all: a critique of human-centred AI
14
Zitationen
1
Autoren
2024
Jahr
Abstract
The use of a 'human-centred' artificial intelligence approach (HCAI) has substantially increased over the past few years in academic texts (1600 +); institutions (27 Universities have HCAI labs, such as Stanford, Sydney, Berkeley, and Chicago); in tech companies (e.g., Microsoft, IBM, and Google); in politics (e.g., G7, G20, UN, EU, and EC); and major institutional bodies (e.g., World Bank, World Economic Forum, UNESCO, and OECD). Intuitively, it sounds very appealing: placing human concerns at the centre of AI development and use. However, this paper will use insights from the works of Michel Foucault (mostly <i>The Order of Things</i>) to argue that the HCAI approach is deeply problematic in its assumptions. In particular, this paper will criticise four main assumptions commonly found within HCAI: human-AI hybridisation is desirable and unproblematic; humans are not currently at the centre of the AI universe; we should use humans as a way to guide AI development; AI is the next step in a continuous path of human progress; and increasing human control over AI will reduce harmful bias. This paper will contribute to the field of philosophy of technology by using Foucault's analysis to examine assumptions found in HCAI [it provides a Foucauldian conceptual analysis of a current approach (human-centredness) that aims to influence the design and development of a transformative technology (AI)], it will contribute to AI ethics debates by offering a critique of human-centredness in AI (by choosing Foucault, it provides a bridge between older ideas with contemporary issues), and it will also contribute to Foucault studies (by using his work to engage in contemporary debates, such as AI).
Ähnliche Arbeiten
The global landscape of AI ethics guidelines
2019 · 4.563 Zit.
The Limitations of Deep Learning in Adversarial Settings
2016 · 3.861 Zit.
Trust in Automation: Designing for Appropriate Reliance
2004 · 3.407 Zit.
Fairness through awareness
2012 · 3.273 Zit.
Mind over Machine: The Power of Human Intuition and Expertise in the Era of the Computer
1987 · 3.183 Zit.