Cited by 201Ethics and Social Impacts of AIHuman-Automation Interaction and SafetySocial Robot Interaction and HRI

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 access

Abbass, Hussein A.·2019·Cognitive Computation

Open this paper in CoChat — free account Open PDF

Abstract

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.

Integrity signals

Registry

Crossref · 10.1007/s12559-018-9619-0

Cites

53 of 53 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

An integrative model of organizational trust
Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995) · The Academy of Management Review · doi:10.2307/258792 · Cited by 10,736
I.—COMPUTING MACHINERY AND INTELLIGENCE
TURING, A. M. (1950) · Mind · doi:10.1093/mind/lix.236.433 · Cited by 10,290
Trust in Automation: Designing for Appropriate Reliance
Lee, J. D., & See, K. A. (2004) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1518/hfes.46.1.50_30392 · Cited by 6,605 · cited 2×
“[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.””
Background
Computing Machinery and Intelligence (1950)
Turing, A. M. (2004) · doi:10.1093/oso/9780198250791.003.0017 · Cited by 6,120
Über das Elektrenkephalogramm des Menschen
Berger, H. (1929) · European Archives of Psychiatry and Clinical Neuroscience · doi:10.1007/bf01797193 · Cited by 4,266
Untitled work
(1979) · Medical Entomology and Zoology · Cited by 3,065
Telerobotics, Automation, and Human Supervisory Control
Sheridan, T. B. (1992) · Medical Entomology and Zoology · Cited by 1,949
Man-Computer Symbiosis
Licklider, J. C. R. (1960) · IRE Transactions on Human Factors in Electronics · doi:10.1109/thfe2.1960.4503259 · Cited by 1,938
THE BERGER RHYTHM: POTENTIAL CHANGES FROM THE OCCIPITAL LOBES IN MAN
ADRIAN, E. D., & MATTHEWS, B. H. C. (1934) · Brain · doi:10.1093/brain/57.4.355 · Cited by 1,396
Toward Direct Brain-Computer Communication
Vidal, J. J. (1973) · Annual Review of Biophysics and Bioengineering · doi:10.1146/annurev.bb.02.060173.001105 · Cited by 1,311

Show all 55 references

Research works printed but not linked: 2

Dragoni N, Giallorenzo S, Lafuente AL, Mazzara M, Montesi F, Mustafin R, Safina L. Microservices: yesterday, today, and tomorrow. Present and Ulterior Software Engineering. Springer; 2017. p. 195–216. · DOI
Endsley MR. Automation and situation awareness. Automation and human performance. Routledge; 2018. p. 183–202.

Sorted by citations

Trust in AI: progress, challenges, and future directions
Afroogh, S., Akbari, A., Malone, E., Kargar, M., & Alambeigi, H. (2024) · Humanities and Social Sciences Communications · doi:10.1057/s41599-024-04044-8 · Cited by 718
Six Human-Centered Artificial Intelligence Grand Challenges
Ozmen Garibay, O., Winslow, B., Andolina, S., Antona, M., Bodenschatz, A., Coursaris, C., Falco, G., Fiore, S. M., Garibay, I., Grieman, K., et al. (2023) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2022.2153320 · Cited by 501
Exploring Artificial Intelligence in Academic Essay: Higher Education Student's Perspective
Malik, A. R., Pratiwi, Y., Andajani, K., Numertayasa, I. W., Suharti, S., Darwis, A., & Marzuki (2023) · International Journal of Educational Research Open · doi:10.1016/j.ijedro.2023.100296 · Cited by 418
Understanding knowledge hiding under technological turbulence caused by artificial intelligence and robotics
Arias-Pérez, J., & Vélez-Jaramillo, J. D. (2021) · Journal of Knowledge Management · doi:10.1108/jkm-01-2021-0058 · Cited by 185
When AI moderates online content: effects of human collaboration and interactive transparency on user trust
Molina, M. D., & Sundar, S. S. (2022) · Journal of Computer-Mediated Communication · doi:10.1093/jcmc/zmac010 · Cited by 179
Explainable Automated Essay Scoring: Deep Learning Really Has Pedagogical Value
Kumar, V., & Boulanger, D. (2020) · Frontiers in Education · doi:10.3389/feduc.2020.572367 · Cited by 148
The effects of personality and locus of control on trust in humans versus artificial intelligence
Sharan, N. N., & Romano, D. M. (2020) · Heliyon · doi:10.1016/j.heliyon.2020.e04572 · Cited by 138
Artificial Intelligence in Business Communication: The Changing Landscape of Research and Teaching
Getchell, K. M., Carradını, S., Cardon, P. W., Fleischmann, C., Ma, H., Aritz, J., & Stapp, J. (2022) · Business and Professional Communication Quarterly · doi:10.1177/23294906221074311 · Cited by 163
Confronting and alleviating AI resistance in the workplace: An integrative review and a process framework
Gölgeci̇, İ., Ritala, P., Arslan, A., McKenna, B., & Ali, I. (2024) · Human Resource Management Review · doi:10.1016/j.hrmr.2024.101075 · Cited by 193
The benefits, risks and bounds of personalizing the alignment of large language models to individuals
Kirk, H. R., Vidgen, B., Röttger, P., & Hale, S. A. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00820-y · Cited by 122
The importance of the assurance that “humans are still in the decision loop” for public trust in artificial intelligence: Evidence from an online experiment
Aoki, N. (2020) · Computers in Human Behavior · doi:10.1016/j.chb.2020.106572 · Cited by 121
Authority Allocation Strategy for Shared Steering Control Considering Human-Machine Mutual Trust Level
Fang, Z., Wang, J., Liang, J., Yan, Y., Pi, D., Zhang, H., & Yin, G. (2023) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2023.3300152 · Cited by 103
FairDrop: Biased Edge Dropout for Enhancing Fairness in Graph Representation Learning
Spinelli, I., Scardapane, S., Hussain, A., & Uncini, A. (2021) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2021.3133818 · Cited by 114
Decoding Gen Z: AI's influence on brand trust and purchasing behavior
Guerra-Tamez, C. R., Flores, K. K., Serna-Mendiburu, G. M., Robles, D. C., & Cortés, J. I. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1323512 · Cited by 163
Cyberethics in nursing education: Ethical implications of artificial intelligence
De Gagne, J. C., Hwang, H., & Jung, D. (2024) · Nursing Ethics · doi:10.1177/09697330231201901 · Cited by 93
Automated Essay Scoring and the Deep Learning Black Box: How Are Rubric Scores Determined?
Kumar, V., & Boulanger, D. (2020) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-020-00211-5 · Cited by 92
Can autonomy level and anthropomorphic characteristics affect public acceptance and trust towards shared autonomous vehicles?
Wu, M., Wang, N., & Yuen, K. F. (2023) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2023.122384 · Cited by 92
Revisiting the role of HR in the age of AI: bringing humans and machines closer together in the workplace
Fenwick, A., Molnár, G., & Frangos, P. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1272823 · Cited by 149
Toward a design theory for virtual companionship
Strohmann, T., Siemon, D., Khosrawi-Rad, B., & Robra-Bissantz, S. (2022) · Human-Computer Interaction · doi:10.1080/07370024.2022.2084620 · Cited by 95
Artificial Intelligence in Religious Education: Ethical, Pedagogical, and Theological Perspectives
Papakostas, C. (2025) · Religions · doi:10.3390/rel16050563 · Cited by 78
Critical success factors for integrating artificial intelligence and robotics
Mir, U. B., Sharma, S. K., Kar, A. K., & Gupta, M. P. (2020) · Digital Policy Regulation and Governance · doi:10.1108/dprg-03-2020-0032 · Cited by 80
One AI Does Not Fit All: A Cluster Analysis of the Laypeople’s Perception of AI Roles
Kim, T., Molina, M. D., Rheu, M. (., Zhan, E. S., & Peng, W. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581340 · Cited by 68
The impact of human-AI collaboration types on consumer evaluation and usage intention: a perspective of responsibility attribution
Yue, B., & Li, H. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2023.1277861 · Cited by 72
Challenges of AI Adoption in the UAE Healthcare
Badi, F. K. A., Hosani, K. A. A., Jabeen, F., Stachowicz-Stanusch, A., Shehzad, N., & Amann, W. (2021) · Vision The Journal of Business Perspective · doi:10.1177/0972262920988398 · Cited by 59
Recorded Business Meetings and AI Algorithmic Tools: Negotiating Privacy Concerns, Psychological Safety, and Control
Cardon, P. W., Ma, H., & Fleischmann, C. (2021) · International Journal of Business Communication · doi:10.1177/23294884211037009 · Cited by 64
Flipping the odds of AI-driven open innovation: The effectiveness of partner trustworthiness in counteracting interorganizational knowledge hiding
Arias-Pérez, J., & Huynh, T. (2023) · Industrial Marketing Management · doi:10.1016/j.indmarman.2023.03.005 · Cited by 53
Human-Swarm-Teaming Transparency and Trust Architecture
Hepworth, A., Baxter, D. P., Hussein, A., Yaxley, K. J., Debie, E., & Abbass, H. A. (2020) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2020.1003545 · Cited by 57
Self-citation
On the Commoditization of Artificial Intelligence
Abonamah, A. A., Tariq, M. U., & Shilbayeh, S. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.696346 · Cited by 69
Knowledge management strategies and organizational improvisation: what changed after the emergence of technological turbulence caused by artificial intelligence?
Arias-Pérez, J., & Cepeda-Cardona, J. (2022) · Baltic Journal of Management · doi:10.1108/bjm-01-2021-0027 · Cited by 48
Understanding the influence of AI autonomy on AI explainability levels in human-AI teams using a mixed methods approach
Hauptman, A. I., Schelble, B. G., Duan, W., Flathmann, C., & McNeese, N. J. (2024) · Cognition Technology & Work · doi:10.1007/s10111-024-00765-7 · Cited by 56
Use of Neural Signals to Evaluate the Quality of Generative Adversarial Network Performance in Facial Image Generation
Wang, Z., Healy, G. F., Smeaton, A. F., & Ward, T. E. (2019) · Cognitive Computation · doi:10.1007/s12559-019-09670-y · Cited by 42
AI Alignment: A Contemporary Survey
Ji, J., Qiu, T., Chen, B., Zhou, J., Zhang, B., Hong, D., Lou, H., Wang, K., Duan, Y., He, Z., et al. (2025) · ACM Computing Surveys · doi:10.1145/3770749 · Cited by 772
Trust or mistrust in algorithmic grading? An embedded agency perspective
Jackson, S., & Panteli, N. (2022) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2022.102555 · Cited by 43
Human trust in otherware – a systematic literature review bringing all antecedents together
Saßmannshausen, T. M., Burggräf, P., Hassenzahl, M., & Wagner, J. (2022) · Ergonomics · doi:10.1080/00140139.2022.2120634 · Cited by 42
Digital capability requirements and improvement strategies: Organizational socialization of AI teammates
Zheng, Q. S., Gou, J., Camarinha-Matos, L. M., Zhang, Z., & Zhang, X. (2023) · Information Processing & Management · doi:10.1016/j.ipm.2023.103504 · Cited by 44
Contextual Awareness in Human-Advanced-Vehicle Systems: A Survey
Rojas, R. F., Perry, A. R., Singh, H. K., Campbell, B., Elsayed, S. M., Hunjet, R., & Abbass, H. A. (2019) · IEEE Access · doi:10.1109/access.2019.2902812 · Cited by 38
Self-citation
How Do Employees Form Initial Trust in Artificial Intelligence: Hard to Explain But Leaders Help
Xu, Y., Huang, Y., Wang, J., & Zhou, D. (2022) · Asia Pacific Journal of Human Resources · doi:10.1111/1744-7941.12402 · Cited by 56
Embedding artificial intelligence in society: looking beyond the EU AI master plan using the culture cycle
Borsci, S., Lehtola, V., Nex, F., Yang, M. Y., Augustijn, E.-W., Bagheriye, L., Brüne, C., Kounadi, O., Li, J. J., Moreira, J., et al. (2022) · AI & Society · doi:10.1007/s00146-021-01383-x · Cited by 44
The ”Artificial” Colleague: Evaluation of Work Satisfaction in Collaboration with Non-human Coworkers
Sadeghian, S., & Hassenzahl, M. (2022) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3490099.3511128 · Cited by 34
The Introduction of AI in the Construction Industry and Its Impact on Human Behavior
Schia, M. H., Trollsås, B. C., Fyhn, H., & Lædre‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌‌, O. (2019) · Annual Conference of the International Group for Lean Construction · doi:10.24928/2019/0191 · Cited by 34
Unlocking agility: Trapped in the antagonism between co-innovation in digital platforms, business analytics capability and external pressure for AI adoption?
Arias-Pérez, J., Chacón-Henao, J., & López-Zapata, E. (2023) · Business Process Management Journal · doi:10.1108/bpmj-10-2022-0484 · Cited by 38
The Soul of Work: Evaluation of Job Meaningfulness and Accountability in Human-AI Collaboration
Sadeghian, S., Uhde, A., & Hassenzahl, M. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3637407 · Cited by 41
Ripples of Change – An AI Job Crafting Model for Human-in-Control
Afiouni, R., & Pinsonneault, A. (2022) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2022.631 · Cited by 30
Human-Machine Duality: What’s Next in Cognitive Aspects of Artificial Intelligence?
Raikov, A., & Pirani, M. (2022) · IEEE Access · doi:10.1109/access.2022.3177657 · Cited by 33
Understanding responsibility under uncertainty: A critical and scoping review of autonomous driving systems
Rowe, F., Medina, M. J., Journé, B., Coëtard, E., & Myers, M. D. (2023) · Journal of Information Technology · doi:10.1177/02683962231207108 · Cited by 40
An Evaluation of Situational Autonomy for Human-AI Collaboration in a Shared Workspace Setting
Salikutluk, V., Schöpper, J., Herbert, F., Scheuermann, K., Frodl, E., Balfanz, D., Jäkel, F., & Koert, D. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642564 · Cited by 40
Analyzing the impact of technology incentives on community digital inclusion using structural equation modeling
Islam, A. Y. M. A., Rafi, M., & Ahmad, K. (2022) · Library Hi Tech · doi:10.1108/lht-07-2021-0226 · Cited by 23
Human-Controllable AI: Meaningful Human Control
Liu, C., & Xu, W. (2025) · Springer eBooks · doi:10.1007/978-981-97-8440-0_58-1 · Cited by 22
Implementation of artificial intelligence in the construction industry and analysis of existing technologies
Kyivska, K., & Tsiutsiura, S. (2021) · Technology audit and production reserves · doi:10.15587/2706-5448.2021.229532 · Cited by 22
A model of symbiomemesis: machine education and communication as pillars for human-autonomy symbiosis
Abbass, H. A., Petraki, E., Hussein, A., McCall, F., & Elsawah, S. (2021) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2020.0364 · Cited by 22
Self-citation
The strategy of traditional Chinese settlement digitization: a landscape gene information chain theory-based perspective
Qier, S. A., Qu, Z., Liu, Y., & Shan, W. (2024) · Heritage Science · doi:10.1186/s40494-024-01342-x · Cited by 22
Benchmarking Human versus Robot Performance in Emergency Structural Inspection
Xia, P., Xu, F., Zhou, T., & Du, J. (2022) · Journal of Construction Engineering and Management · doi:10.1061/(asce)co.1943-7862.0002322 · Cited by 21
Assessing human-centricity in AI enabled manufacturing systems: a socio-technical evaluation methodology
Waschull, S., & Emmanouilidis, C. (2023) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2023.10.1891 · Cited by 21
From awareness to action: unraveling the interplay of employee AI awareness and change leadership in fostering knowledge hiding
Iram, T., Bilal, A. R., Khan, R. A., Mehmood, S., & R, H. K. G. (2024) · Kybernetes · doi:10.1108/k-01-2024-0222 · Cited by 18
Respecting Human Autonomy through Human-Centered AI
Sankaran, S., Zhang, C., Lopez, M. G., & Väänänen, K. (2020) · Nordic Conference on Human-Computer Interaction · doi:10.1145/3419249.3420098 · Cited by 18
The Medical Authority of AI: A Study of AI-enabled Consumer-Facing Health Technology
You, Y., Kou, Y., Ding, X., & Gui, X. (2021) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445657 · Cited by 21
”It’s like a puppet master”: User Perceptions of Personal Autonomy when Interacting with Intelligent Technologies
Sankaran, S., & Markopoulos, P. (2021) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3450613.3456820 · Cited by 18
Young peoples’ reflections about using a chatbot to promote their mental wellbeing in northern periphery areas - a qualitative study
Kostenius, C., Lindström, F., Potts, C., & Pekkari, N. (2024) · International Journal of Circumpolar Health · doi:10.1080/22423982.2024.2369349 · Cited by 18
Do I have a say?
Sankaran, S., Zhang, C., Funk, M., Aarts, H., & Markopoulos, P. (2020) · ACM Conference on Conversational User Interfaces (CUI) · doi:10.1145/3405755.3406135 · Cited by 18
Hybrid Teamwork: Consideration of Teamwork Concepts to Reach Naturalistic Interaction between Humans and Conversational Agents
Poser, M., & Bittner, E. A. C. (2020) · doi:10.30844/wi_2020_a6-poser · Cited by 17
Human Dynamics of Automation and Digitalisation of Economies: Discussion on the Challenges and Opportunities
Arslan, A., Ruman, A. M., Naughton, S., & Tarba, S. Y. (2020) · Springer eBooks · doi:10.1007/978-3-030-42412-1_31 · Cited by 17
Exploring the Impact of Artificial Intelligence-Generated Content (AIGC) Tools on Social Dynamics in UX Collaboration
Wang, Z., Shen, L., Kuang, E., Zhang, S., & Fan, M. (2024) · Designing Interactive Systems Conference · doi:10.1145/3643834.3660703 · Cited by 21
Human–AI Co-Drawing: Studying Creative Efficacy and Eye Tracking in Observation and Cooperation
Pei, Y., Wang, L., & Xue, C. (2024) · Applied Sciences · doi:10.3390/app14188203 · Cited by 16
The Journey or the Destination: The Impact of Transparency and Goal Attainment on Trust in Human-Robot Teams
Kox, E. S., van den Boogaard, J., Turjaka, V., & Kerstholt, J. H. (2024) · ACM Transactions on Human-Robot Interaction · doi:10.1145/3702245 · Cited by 18
Future of Work: How Artificial Intelligence Will Change the Dynamics of Work Culture and Influence Employees Work Satisfaction Post-covid-19
Singh, R., & Tarkar, P. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-19-0976-4_21 · Cited by 15
Exploring professional translators’ attitudes towards control and autonomy in the human-centred AI era: quantitative results from a survey study
Jiménez-Crespo, M. A. (2024) · Tradumàtica tecnologies de la traducció · doi:10.5565/rev/tradumatica.400 · Cited by 15
Rewritable and Sustainable 2D Barcode for Traceability Application in Smart IoT Based Fault-Tolerant Mechanism
Chen, R., Yu, Y., Xie, S., Zhao, H., Liu, S., Ren, J., & Tan, H. (2020) · Sustainability · doi:10.3390/su12177192 · Cited by 15
Towards Trust-Aware Human-Automation Interaction: An Overview of the Potential of Computational Trust Models
Hussein, A., Elsawah, S., & Abbass, H. (2020) · Hawaii International Conference on System Sciences · doi:10.24251/hicss.2020.047 · Cited by 15
Self-citation
Voice-based personal assistant (VPA) trust: Investigating competence and integrity
Huang, R., Kim, M., & Lennon, S. J. (2024) · Telematics and Informatics Reports · doi:10.1016/j.teler.2024.100140 · Cited by 17
Resilient by Design: Exploring the Social Abilities and Actor‐Network Roles of Artificial Intelligence in Supply Chain Management
Condé, L., & Münch, C. (2025) · Journal of Business Logistics · doi:10.1111/jbl.70032 · Cited by 21
Teamwork doesn’t just happen: Policy recommendations from over half a century of team research
Grote, G., & Kozlowski, S. W. J. (2023) · Behavioral Science & Policy · doi:10.1177/23794607231192734 · Cited by 18
Do We Trust Artificially Intelligent Assistants at Work? An Experimental Study
Cvetkovic, A., Savela, N., Latikka, R., & Oksanen, A. (2024) · Human Behavior and Emerging Technologies · doi:10.1155/2024/1602237 · Cited by 14
HUMAN FACTORS AND ERGONOMICS IN DESIGN OF A3: AUTOMATION, AUTONOMY, AND ARTIFICIAL INTELLIGENCE
Sawyer, B. D., Miller, D., Canham, M., & Karwowski, W. (2021) · Wiley eBooks · doi:10.1002/9781119636113.ch52 · Cited by 13
Product Engineering Assessment of Subsea Intervention Equipment Using SWARA-MOORA-3NAG Method
Fernandes, P. G., Quelhas, O. L. G., Gomes, C. F. S., Júnior, E. L. P., Bella, R. L. F., Rocha, C. D. S., Pereira, R. C. A., Basílio, M. P., & dos Santos, M. (2023) · Systems · doi:10.3390/systems11030125 · Cited by 11
Artificial Intelligence and Society: Pros and Cons of the Present, Future Prospects
Nikolenko, K. (2022) · Futurity Philosophy. · doi:10.57125/fp.2022.06.30.05 · Cited by 11
Machines think, but do we? Embracing AI in flat organizations: Challenges of technological change in women-owned manufacturing firms
Iram, T., Malik, H. A., Batool, S., Bano, A., Ng, B., & Bilal, A. R. (2024) · Information Development · doi:10.1177/02666669241291568 · Cited by 19
Human Autonomy Teaming for ROV Shared Control
Xia, P., Zhou, T., Ye, Y., & Du, J. (2024) · Journal of Computing in Civil Engineering · doi:10.1061/jccee5.cpeng-5756 · Cited by 11
Construction of an AI-Driven Risk Management Framework for Financial Service Firms Using the MRDM Approach
Hu, K.-H., Chen, F.-H., Hsu, M.-F., & Tzeng, G. (2021) · International Journal of Information Technology & Decision Making · doi:10.1142/s0219622021500279 · Cited by 14
How to Keep Balance between Interaction and Automation? Toward User Overall Positive Experience of IoT-Based Smart Home Design
Xu, H., Lee, H., Ling, W., & Pan, Y. (2024) · Electronics · doi:10.3390/electronics13071375 · Cited by 9
Decoding user readiness for sustainable AI adoption: A behavioural approach through technology readiness segmentation (TRS)
Ghosh, M. (2025) · Sustainable Futures · doi:10.1016/j.sftr.2025.100951 · Cited by 13
The Bermuda Triangle of Leadership in the AI Era? Emerging Trust Implications From “Two-Leader-Situations” in the Eyes of Employees
Schafheitle, S. D., Weibel, A. A., & Rickert, A. (2021) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2021.665 · Cited by 10
Human Robot Comparison in Rapid Structural Inspection
Xia, P., Xu, F., Zhu, Q., & Du, J. (2022) · ASCE Construction Research Congress (CRC) · doi:10.1061/9780784483961.060 · Cited by 9
Editorial: Framing the Future with a Research Agenda for Artificial Intelligence in Higher Education
Fitzgerald, R., Amantha Kumar, J., Roe, J., Roehrer, E., & Yang, J. (2025) · Journal of University Teaching and Learning Practice · doi:10.53761/jwt7ra63 · Cited by 8
Towards a systematic educational framework for human-machine teaming
McCall, F., Hussein, A., Petraki, E., Elsawah, S., & Abbass, H. A. (2021) · IEEE International Conference on Teaching, Assessment, and Learning for Engineering · doi:10.1109/tale52509.2021.9678853 · Cited by 8
Self-citation
Augmented Humanity
Bryant, P. (2021) · Springer eBooks · doi:10.1007/978-3-030-76445-6 · Cited by 8
Telehealth and Telemedicine in Latin American Rheumatology, a New Era After COVID-19
Graf, C., Fernández-Ávila, D. G., Plazzotta, F., & Soriano, E. R. (2023) · JCR Journal of Clinical Rheumatology · doi:10.1097/rhu.0000000000001968 · Cited by 10
Effortlessness as the measurement of the impact of artificial intelligence (AI) adoption and innovation: Towards the theory of relative AI
Sohn, S. M. (2025) · Technology in Society · doi:10.1016/j.techsoc.2025.102962 · Cited by 11
Leveraging Usefulness and Autonomy: Designing AI-Mediated ASL Communication Between Hearing Parents and Deaf Children
Li, Y., Wang, H., Hossain, E., Mann, M., Yu, J., Newman, K. S., Bao, A., Willis, A. S., Kurumada, C., Hall, W. C., et al. (2025) · ACM International Conference on Interaction Design and Children (IDC) · doi:10.1145/3713043.3727054 · Cited by 6
How and when organizational AI adoption improves employee innovation performance
Hu, W., Wang, L., Ren, J., Bai, G., Cui, S., & Xu, Y. (2026) · Service Industries Journal · doi:10.1080/02642069.2026.2632108 · Cited by 8
The Need for a Human Centric Approach in C4.0 Technologies
Noueihed, K., & Hamzeh, F. (2022) · Annual Conference of the International Group for Lean Construction · doi:10.24928/2022/0194 · Cited by 7
HCI driving alienation: autonomy and involvement as blind spots in digital ethics
Jungtäubl, M., Zirnig, C., & Ruiner, C. (2023) · AI and Ethics · doi:10.1007/s43681-023-00298-1 · Cited by 6
Augmented Intelligence: A Comprehensive Review of the Flexibility Between Human and Artificial Intelligence
Mohamed, N. A. (2025) · Journal of The Institution of Engineers (India) Series B · doi:10.1007/s40031-025-01213-4 · Cited by 10
The Automation of Management Decisions: A Systematic Review and Research Agenda of the Factors Influencing the Decision to Increase the Level of Automation
Rossmann, L., & Wald, A. E. (2022) · International Journal of Information Technology & Decision Making · doi:10.1142/s021962202330001x · Cited by 7
NDE 4.0: New Paradigm for the NDE Inspection Personnel
Bertovic, M., & Virkkunen, I. (2021) · Springer eBooks · doi:10.1007/978-3-030-48200-8_9-1 · Cited by 7
NDE 4.0: New Paradigm for the NDE Inspection Personnel
Bertovic, M., & Virkkunen, I. (2022) · Springer eBooks · doi:10.1007/978-3-030-73206-6_9 · Cited by 6
Exploiting AI's Potential in Knowledge Management
Salem, M., Salloum, S. A., & Shaalan, K. F. (2024) · Studies in big data · doi:10.1007/978-3-031-52280-2_18 · Cited by 6
The Meta Holonic Management Tree: review, steps, and roadmap to industrial Cybernetics 5.0
Pirani, M., Carbonari, A., Cucchiarelli, A., Giretti, A., & Spalazzi, L. (2024) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-024-02510-3 · Cited by 7
The effect of workload on mind-wandering of drilling operators measured by electroencephalography (EEG)
Hao, S., Ruiying, X., Lifei, X., Jian, W., Jiaxin, J., Siping, F., Xiaoqin, W., Xin, Q., Lu, L., & Zhang, Y. (2024) · Experimental Brain Research · doi:10.1007/s00221-024-06974-w · Cited by 5
Team Design Patterns for Meaningful Human Control in Responsible Military Artificial Intelligence
van Diggelen, J., Burken, C. B.-V., & Abbass, H. A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-75434-0_4 · Cited by 5
Self-citation
Visual Analytics Review: An Early and Continuing Success of Convergent Research With Impact
Ebert, D. S., Reinert, A., & Fisher, B. (2021) · Computing in Science & Engineering · doi:10.1109/mcse.2021.3069342 · Cited by 6

Authors: Abbass, H. A.
Affiliations: UNSW Canberra (AU)
Corresponding author: Abbass, Hussein A.
ORCID: Abbass, Hussein A.
Venue: Cognitive Computation
Publisher: Springer Science and Business Media LLC
Published: 2019-01-14
Volume: 11
Issue: 2
Pages: 159-171
ISSN: 1866-9956 (ISSN-L)
1866-9964
DOI: 10.1007/s12559-018-9619-0
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
Licence: cc-by
creativecommons.org/licenses/by/4.0 (tdm)
creativecommons.org/licenses/by/4.0 (vor)
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Social Integration of Artificial Intelligence: Functions, Automation Allocation Logic and Human-Autonomy Trust

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks