Technology Category
Humans and AI: Other Foundations of Human Computation & AICognitive Modeling & Cognitive Systems: Simulating Human BehaviorPhilosophy and Ethics of AI: Bias, Fairness & Equity
What Does 'Human-Centred AI' Mean?
🤖 AI Summary
This paper critically examines the implementation gap in “human-centered AI” (HCAI), identifying three pervasive issues: the occlusion of human cognitive labor, technological fetishism, and the black-boxing of cognitive processes in contemporary AI systems. Drawing on socio-technical systems theory, it employs conceptual analysis and cross-historical comparative case studies—from the abacus to deep neural networks—to propose a novel triadic framework: *displacement–augmentation–replacement*. This framework systematically classifies modes of interaction between AI and human cognitive labor, demystifying AI by establishing the foundational premise that *all AI inherently embeds human cognitive participation*. It further exposes how neglecting the cognitive dimension leads to scientific mischaracterizations and engineering biases. The study contributes a critical, actionable design paradigm for HCAI, advancing a paradigmatic shift from technology-centric to cognition-cooperative AI development.
Technology Category
Application Category
📝 Abstract
While it seems sensible that human-centred artificial intelligence (AI) means centring "human behaviour and experience," it cannot be any other way. AI, I argue, is usefully seen as a relationship between technology and humans where it appears that artifacts can perform, to a greater or lesser extent, human cognitive labour. This is evinced using examples that juxtapose technology with cognition, inter alia: abacus versus mental arithmetic; alarm clock versus knocker-upper; camera versus vision; and sweatshop versus tailor. Using novel definitions and analyses, sociotechnical relationships can be analysed into varying types of: displacement (harmful), enhancement (beneficial), and/or replacement (neutral) of human cognitive labour. Ultimately, all AI implicates human cognition; no matter what. Obfuscation of cognition in the AI context -- from clocks to artificial neural networks -- results in distortion, in slowing critical engagement, perverting cognitive science, and indeed in limiting our ability to truly centre humans and humanity in the engineering of AI systems. To even begin to de-fetishise AI, we must look the human-in-the-loop in the eyes.
Problem
Research questions and friction points this paper is trying to address.
Defining human-centered AI and its implications
Analyzing AI's impact on human cognitive labor
Addressing obfuscation of cognition in AI systems
Innovation
Methods, ideas, or system contributions that make the work stand out.
Analyzes AI-human relationships via displacement, enhancement, replacement
Uses novel definitions to classify sociotechnical impacts
Emphasizes human cognition in AI system engineering
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