What Does 'Human-Centred AI' Mean?
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 14 of its 158 references that are in the map. First posted on arXiv 26 Jul 2025.
Builds on
14 of 158 references.
References · 14
- 2021AI and the Everything in the Whole Wide World Benchmarkcited by 349
- 2022The Fallacy of AI Functionalitycited by 88
- 2025Can We Trust AI Benchmarks? An Interdisciplinary Review of Current Issues in AI Evaluationcited by 60
- 2023Opening up ChatGPT: Tracking openness, transparency, and accountability in instruction-tuned text generatorscited by 56
- 2023Into the LAIONs Den: Investigating Hate in Multimodal Datasetscited by 54
- 2025Investigating Affective Use and Emotional Well-being on ChatGPTcited by 38
- 2024Towards AI Accountability Infrastructure: Gaps and Opportunities in AI Audit Toolingcited by 29
- 2022On the Impossible Safety of Large AI Modelscited by 28
- 2023On Hate Scaling Laws For Data-Swampscited by 28
- 2024The Dark Side of Dataset Scaling: Evaluating Racial Classification in Multimodal Modelscited by 23
- 2020Artificial Intelligence is stupid and causal reasoning won't fix itcited by 20
- 2020Towards decolonising computational sciencescited by 17
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Citing papers per year: 2026: 2.
2 papers.