The DOI matches its Crossref record; 53 of the 55 references it cites were found in a registry or an index.
Social Integration of Artificial Intelligence: Functions, Automation Allocation Logic and Human-Autonomy Trust
Open accessAbstract
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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“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]
Citations
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“[48, p. 712] Lee and See “the attitude that an agent will help achieve an individual’s goals in a situation characterised by uncertainty and vulnerability.””
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Research works printed but not linked: 2
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Items 201–201 of 201
Designing cognition-driven Human-AI teaming: A Three-Layer Cognitive Framework (TriC)
Yin, Y., Jin, E., Pan, H., (324865), X. J., Zhang, Y., Liu, Y., Shen, M., & Gao, Z. (2026) · Computers in Human Behavior Reports · doi:10.2139/ssrn.6890561
Metadata
- 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
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Human Factors and Ergonomics › 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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