Social Integration of Artificial Intelligence: Functions, Automation Allocation Logic and Human-Autonomy Trust
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Artificial intelligence (AI) is finding more uses in the human society resulting in a need to scrutinise the relationship between humans and AI. Technology itself has advanced from the mere encoding of human knowledge into a machine to designing machines that “know how” to autonomously acquire the knowledge they need, learn from it and act independently in the environment. Fortunately, this need is not new; it has scientific grounds that could be traced back to the inception of computers. This paper uses a multi-disciplinary lens to explore how the natural cognitive intelligence in a human could interface with the artificial cognitive intelligence of a machine. The scientific journey over the last 50 years will be examined to understand the Human-AI relationship, and to present the nature of, and the role of trust in, this relationship. Risks and opportunities sitting at the human-AI interface will be studied to reveal some of the fundamental technical challenges for a trustworthy human-AI relationship. The critical assessment of the literature leads to the conclusion that any social integration of AI into the human social system would necessitate a form of a relationship on one level or another in society, meaning that humans will “always” actively participate in certain decision-making loops—either in-the-loop or on-the-loop—that will influence the operations of AI, regardless of how sophisticated it is.
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Measurement invariance is impaired because AI detection classifiers systematically associate linguistic predictability with artificial authorship.
“we can conclude that measurement invariance is impaired. While the use of algorithmic classifiers systematically associates linguistic predictability and artificial authorship, the association introduces CIV [43] is not relevant to CRC.”
From The impact of generative artificial intelligence tools on assessment equity for non-native English-speaking medical students: a systematic review (2026) · cites this paper as [43]
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- Authors
- Abbass, H. A.
- Affiliations
- UNSW Canberra (AU)
- Corresponding author
- Abbass, Hussein A.
- ORCID
- Abbass, Hussein A.
- Venue
- Cognitive Computation
- Published
- 2019-01-14
- Volume
- 11
- Issue
- 2
- Pages
- 159-171
- ISSN
- 1866-9956 (ISSN-L)
- 1866-9964
- OpenAlex
- W2909340128
- Cited by
- 200
- References
- 55
- Citations by year
- 2026: 34 · 2025: 35 · 2024: 36 · 2023: 29 · 2022: 28 · 2021: 19 · 2020: 16 · 2019: 3
- FWCI
- 28.86
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Cognitive Computation (publishedVersion, cc-by)
- Funders
- Australian Research Council (DP160102037)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Keywords
- human-AI relationship, trust in AI, human-AI interface, human autonomy, human-AI collaboration, AI decision-making
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