What Does 'Human-Centred AI' Mean?

• URL: http://arxiv.org/abs/2507.19960v2
• Date: Tue, 29 Jul 2025 12:19:01 GMT
• Title: What Does 'Human-Centred AI' Mean?
• Authors: Olivia Guest,
• Abstract summary: AI is usefully seen as a relationship between technology and humans.<n>All AI implicates human cognition; no matter what.<n>To even begin to de-fetishise AI, we must look the human-in-the-loop in the eyes.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Aligning Generalisation Between Humans and Machines [74.120848518198]
  AI technology can support humans in scientific discovery and forming decisions, but may also disrupt democracies and target individuals.<n>The responsible use of AI and its participation in human-AI teams increasingly shows the need for AI alignment.<n>A crucial yet often overlooked aspect of these interactions is the different ways in which humans and machines generalise.
  arXiv  Detail & Related papers  (2024-11-23T18:36:07Z)
• Imagining and building wise machines: The centrality of AI metacognition [78.76893632793497]
  We examine what is known about human wisdom and sketch a vision of its AI counterpart.<n>We argue that AI systems particularly struggle with metacognition.<n>We discuss how wise AI might be benchmarked, trained, and implemented.
  arXiv  Detail & Related papers  (2024-11-04T18:10:10Z)
• Rolling in the deep of cognitive and AI biases [1.556153237434314]
  We argue that there is urgent need to understand AI as a sociotechnical system, inseparable from the conditions in which it is designed, developed and deployed. We address this critical issue by following a radical new methodology under which human cognitive biases become core entities in our AI fairness overview. We introduce a new mapping, which justifies the humans to AI biases and we detect relevant fairness intensities and inter-dependencies.
  arXiv  Detail & Related papers  (2024-07-30T21:34:04Z)
• Explainable Human-AI Interaction: A Planning Perspective [32.477369282996385]
  AI systems need to be explainable to the humans in the loop. We will discuss how the AI agent can use mental models to either conform to human expectations, or change those expectations through explanatory communication. While the main focus of the book is on cooperative scenarios, we will point out how the same mental models can be used for obfuscation and deception.
  arXiv  Detail & Related papers  (2024-05-19T22:22:21Z)
• A Review on Objective-Driven Artificial Intelligence [0.0]
  Humans have an innate ability to understand context, nuances, and subtle cues in communication. Humans possess a vast repository of common-sense knowledge that helps us make logical inferences and predictions about the world. Machines lack this innate understanding and often struggle with making sense of situations that humans find trivial.
  arXiv  Detail & Related papers  (2023-08-20T02:07:42Z)
• Cybertrust: From Explainable to Actionable and Interpretable AI (AI2) [58.981120701284816]
  Actionable and Interpretable AI (AI2) will incorporate explicit quantifications and visualizations of user confidence in AI recommendations. It will allow examining and testing of AI system predictions to establish a basis for trust in the systems' decision making.
  arXiv  Detail & Related papers  (2022-01-26T18:53:09Z)
• On some Foundational Aspects of Human-Centered Artificial Intelligence [52.03866242565846]
  There is no clear definition of what is meant by Human Centered Artificial Intelligence. This paper introduces the term HCAI agent to refer to any physical or software computational agent equipped with AI components. We see the notion of HCAI agent, together with its components and functions, as a way to bridge the technical and non-technical discussions on human-centered AI.
  arXiv  Detail & Related papers  (2021-12-29T09:58:59Z)
• Trustworthy AI: A Computational Perspective [54.80482955088197]
  We focus on six of the most crucial dimensions in achieving trustworthy AI: (i) Safety & Robustness, (ii) Non-discrimination & Fairness, (iii) Explainability, (iv) Privacy, (v) Accountability & Auditability, and (vi) Environmental Well-Being. For each dimension, we review the recent related technologies according to a taxonomy and summarize their applications in real-world systems.
  arXiv  Detail & Related papers  (2021-07-12T14:21:46Z)
• Joint Inference of States, Robot Knowledge, and Human (False-)Beliefs [90.20235972293801]
  Aiming to understand how human (false-temporal)-belief-a core socio-cognitive ability unify-would affect human interactions with robots, this paper proposes to adopt a graphical model to the representation of object states, robot knowledge, and human (false-)beliefs. An inference algorithm is derived to fuse individual pg from all robots across multi-views into a joint pg, which affords more effective reasoning inference capability to overcome the errors originated from a single view.
  arXiv  Detail & Related papers  (2020-04-25T23:02:04Z)
• Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense [142.53911271465344]
  We argue that the next generation of AI must embrace "dark" humanlike common sense for solving novel tasks. We identify functionality, physics, intent, causality, and utility (FPICU) as the five core domains of cognitive AI with humanlike common sense.
  arXiv  Detail & Related papers  (2020-04-20T04:07:28Z)
• Human Evaluation of Interpretability: The Case of AI-Generated Music Knowledge [19.508678969335882]
  We focus on evaluating AI-discovered knowledge/rules in the arts and humanities. We present an experimental procedure to collect and assess human-generated verbal interpretations of AI-generated music theory/rules rendered as sophisticated symbolic/numeric objects.
  arXiv  Detail & Related papers  (2020-04-15T06:03:34Z)
• Cognitive Anthropomorphism of AI: How Humans and Computers Classify Images [0.0]
  Humans engage in cognitive anthropomorphism: expecting AI to have the same nature as human intelligence. This mismatch presents an obstacle to appropriate human-AI interaction. I offer three strategies for system design that can address the mismatch between human and AI classification.
  arXiv  Detail & Related papers  (2020-02-07T21:49:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.