Fetching the paper…
Reading the bibliography…
The use of artificial intelligence (AI) in working environments with individuals, known as Human-AI Collaboration (HAIC), has become essential in a variety of domains, boosting decision-making, efficiency, and innovation.
B. Shneiderman, “Direct manipulation: A step beyond programming languages,” Computer , vol. 16, no. 08, pp. 57–69, 1983
1983
Earlier work this paper cites.
W. B. Rouse, N. D. Geddes, and R. E. Curry, “An architecture for intelligent interfaces: Outline of an approach to supporting operators of complex systems,” Human-computer interaction , vol. 3, no. 2, pp. 87–122, 1987
1987
Earlier work this paper cites.
J. Nielsen, Usability engineering . Morgan Kaufmann, 1994
1994
Earlier work this paper cites.
J. D. Lee and N. Moray, “Trust, self-confidence, and operators’ adaptation to automation,” International journal of human-computer studies , vol. 40, no. 1, pp. 153–184, 1994
1994
Earlier work this paper cites.
G. Fischer, “Rethinking and reinventing artificial intelligence from the perspective of human-centered computational artifacts,” in Brazilian Symposium on Artificial Intelligence . Springer, 1995, pp. 1–11
1995
Earlier work this paper cites.
W. Iso, “9241-11. ergonomic requirements for office work with visual display terminals (vdts),” The international organization for standardization , vol. 45, no. 9, 1998
1998
Earlier work this paper cites.
E. Horvitz, “Principles of mixed-initiative user interfaces,” in Proceedings of the SIGCHI conference on Human Factors in Computing Systems , 1999, pp. 159–166
1999
Earlier work this paper cites.
R. Parasuraman, T. B. Sheridan, and C. D. Wickens, “A model for types and levels of human interaction with automation,” IEEE Transactions on systems, man, and cybernetics-Part A: Systems and Humans , vol. 30, no. 3, pp. 286–297, 2000
2000
Earlier work this paper cites.
W. Kuo and V. R. Prasad, “An annotated overview of system-reliability optimization,” IEEE Transactions on reliability , vol. 49, no. 2, pp. 176–187, 2000
2000
Earlier work this paper cites.
J.-M. Hoc, “From human–machine interaction to human–machine cooperation,” Ergonomics , vol. 43, no. 7, pp. 833–843, 2000
2000
Earlier work this paper cites.
K. Dautenhahn, “Socially intelligent robots: dimensions of human–robot interaction,” Philosophical transactions of the royal society B: Biological sciences , vol. 362, no. 1480, pp. 679–704, 2007
2007
Earlier work this paper cites.
F. Magrabi, M.-S. Ong, W. Runciman, and E. Coiera, “An analysis of computer-related patient safety incidents to inform the development of a classification,” Journal of the American Medical Informatics Association , vol. 17, no. 6, pp. 663–670, 2010
2010
Earlier work this paper cites.
R. Fiebrink, P. R. Cook, and D. Trueman, “Human model evaluation in interactive supervised learning,” pp. 147–156, 2011
2011
Earlier work this paper cites.
E. Kamar, S. Hacker, and E. Horvitz, “Combining human and machine intelligence in large-scale crowdsourcing.” in AAMAS , vol. 12, 2012, pp. 467–474
2012
Earlier work this paper cites.
D. Norman, The design of everyday things: Revised and expanded edition . Basic books, 2013
2013
Earlier work this paper cites.
M. Cakmak and A. L. Thomaz, “Eliciting good teaching from humans for machine learners,” Artificial Intelligence , vol. 217, pp. 198–215, 2014
2014
Earlier work this paper cites.
S. Amershi, M. Cakmak, W. B. Knox, and T. Kulesza, “Power to the people: The role of humans in interactive machine learning,” Ai Magazine , vol. 35, no. 4, pp. 105–120, 2014
2014
Earlier work this paper cites.
A. Cesta, A. Orlandini, G. Bernardi, and A. Umbrico, “Towards a planning-based framework for symbiotic human-robot collaboration,” in 2016 IEEE 21st international conference on emerging technologies and factory automation (ETFA) . IEEE, 2016, pp. 1–8
2016
Earlier work this paper cites.
H. Kerzner, Project management: a systems approach to planning, scheduling, and controlling . John Wiley & Sons, 2017
2017
Earlier work this paper cites.
P. Tsarouchi, A.-S. Matthaiakis, S. Makris, and G. Chryssolouris, “On a human-robot collaboration in an assembly cell,” International Journal of Computer Integrated Manufacturing , vol. 30, no. 6, pp. 580–589, 2017
2017
Earlier work this paper cites.
A. Lui and G. W. Lamb, “Artificial intelligence and augmented intelligence collaboration: regaining trust and confidence in the financial sector,” Information & Communications Technology Law , vol. 27, no. 3, pp. 267–283, 2018
2018
Earlier work this paper cites.
J. W. Crandall, M. Oudah, Tennom, F. Ishowo-Oloko, S. Abdallah, J.-F. Bonnefon, M. Cebrian, A. Shariff, M. A. Goodrich, and I. Rahwan, “Cooperating with machines,” Nature Communications , vol. 9, no. 1, Jan. 2018. [Online]. Available: http://dx.doi.org/10.1038/s41467-017-02597-8
2018
Earlier work this paper cites.
S. Amershi, D. Weld, M. Vorvoreanu, A. Fourney, B. Nushi, P. Collisson, J. Suh, S. Iqbal, P. N. Bennett, K. Inkpen et al. , “Guidelines for human-ai interaction,” in Proceedings of the 2019 chi conference on human factors in computing systems , 2019, pp. 1–13
2019
Earlier work this paper cites.
K. Jokinen and K. Watanabe, “Boundary-crossing robots: Societal impact of interactions with socially capable autonomous agents,” in Social Robotics: 11th International Conference, ICSR 2019, Madrid, Spain, November 26–29, 2019, Proceedings 11 . Springer, 2019, pp. 3–13
2019
Earlier work this paper cites.
K. Holstein, J. Wortman Vaughan, H. Daumé III, M. Dudik, and H. Wallach, “Improving fairness in machine learning systems: What do industry practitioners need?” in Proceedings of the 2019 CHI conference on human factors in computing systems , 2019, pp. 1–16
2019
Earlier work this paper cites.
X. Liu, L. Faes, A. U. Kale, S. K. Wagner, D. J. Fu, A. Bruynseels, T. Mahendiran, G. Moraes, M. Shamdas, C. Kern et al. , “A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis,” The lancet digital health , vol. 1, no. 6, pp. e271–e297, 2019
2019
Earlier work this paper cites.
E. E. Makarius, D. Mukherjee, J. D. Fox, and A. K. Fox, “Rising with the machines: A sociotechnical framework for bringing artificial intelligence into the organization,” Journal of Business Research , vol. 120, pp. 262–273, 2020
2020
Earlier work this paper cites.
P. Tschandl, C. Rinner, Z. Apalla, G. Argenziano, N. Codella, A. Halpern, M. Janda, A. Lallas, C. Longo, J. Malvehy et al. , “Human–computer collaboration for skin cancer recognition,” Nature Medicine , vol. 26, no. 8, pp. 1229–1234, 2020
2020
Earlier work this paper cites.
K. Okamura and S. Yamada, “Adaptive trust calibration for human-ai collaboration,” Plos one , vol. 15, no. 2, p. e0229132, 2020
2020
Earlier work this paper cites.
I. Seeber, E. Bittner, R. O. Briggs, T. De Vreede, G.-J. De Vreede, A. Elkins, R. Maier, A. B. Merz, S. Oeste-Reiß, N. Randrup et al. , “Machines as teammates: A research agenda on ai in team collaboration,” Information & management , vol. 57, no. 2, p. 103174, 2020
2020
Earlier work this paper cites.
A. Dubey, K. Abhinav, S. Jain, V. Arora, and A. Puttaveerana, “Haco: a framework for developing human-ai teaming,” in Proceedings of the 13th Innovations in Software Engineering Conference on Formerly known as India Software Engineering Conference , 2020, pp. 1–9
2020
Earlier work this paper cites.
M.-L. How, S.-M. Cheah, Y.-J. Chan, A. C. Khor, and E. M. P. Say, “Artificial intelligence-enhanced decision support for informing global sustainable development: A human-centric ai-thinking approach,” Information , vol. 11, no. 1, p. 39, 2020
2020
Earlier work this paper cites.
D. Wang, E. Churchill, P. Maes, X. Fan, B. Shneiderman, Y. Shi, and Q. Wang, “From human-human collaboration to human-ai collaboration: Designing ai systems that can work together with people,” in Extended abstracts of the 2020 CHI conference on human factors in computing systems , 2020, pp. 1–6
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
J. Wenskovitch and C. North, “Interactive artificial intelligence: designing for the" two black boxes" problem,” Computer , vol. 53, no. 8, pp. 29–39, 2020
2020
Earlier work this paper cites.
Y. Zhang, Q. V. Liao, and R. K. Bellamy, “Effect of confidence and explanation on accuracy and trust calibration in ai-assisted decision making,” in Proceedings of the 2020 conference on fairness, accountability, and transparency , 2020, pp. 295–305
2020
Earlier work this paper cites.
M. K. M. Rabby, M. A. Khan, A. Karimoddini, and S. X. Jiang, “Modeling of trust within a human-robot collaboration framework,” in 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) . IEEE, 2020, pp. 4267–4272
2020
Earlier work this paper cites.
F. Mohammadi Amin, M. Rezayati, H. W. van de Venn, and H. Karimpour, “A mixed-perception approach for safe human–robot collaboration in industrial automation,” Sensors , vol. 20, no. 21, p. 6347, 2020
2020
Earlier work this paper cites.
C. Emmanouilidis, S. Waschull, J. A. Bokhorst, and J. C. Wortmann, “Human in the ai loop in production environments,” in Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems: IFIP WG 5.7 International Conference, APMS 2021, Nantes, France, September 5–9, 2021, Proceedings, Part IV . Springer, 2021, pp. 331–342
2021
Earlier work this paper cites.
R. P. Buckley, D. A. Zetzsche, D. W. Arner, and B. W. Tang, “Regulating artificial intelligence in finance: Putting the human in the loop,” Sydney Law Review, The , vol. 43, no. 1, pp. 43–81, 2021
2021
Earlier work this paper cites.
Y. Lai, A. Kankanhalli, and D. Ong, “Human-ai collaboration in healthcare: A review and research agenda,” 2021
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
S. Sutthithatip, S. Perinpanayagam, S. Aslam, and A. Wileman, “Explainable ai in aerospace for enhanced system performance,” in 2021 IEEE/AIAA 40th Digital Avionics Systems Conference (DASC) . IEEE, 2021, pp. 1–7
2021
Earlier work this paper cites.
K. Sowa, A. Przegalinska, and L. Ciechanowski, “Cobots in knowledge work: Human–ai collaboration in managerial professions,” Journal of Business Research , vol. 125, pp. 135–142, 2021
2021
Earlier work this paper cites.
J. Liu and Y. Zhao, “Role-oriented task allocation in human-machine collaboration system,” in 2021 IEEE 4th International Conference on Information Systems and Computer Aided Education (ICISCAE) . IEEE, 2021, pp. 243–248
2021
Earlier work this paper cites.
H. C. Siu, J. Peña, E. Chen, Y. Zhou, V. Lopez, K. Palko, K. Chang, and R. Allen, “Evaluation of human-ai teams for learned and rule-based agents in hanabi,” Advances in Neural Information Processing Systems , vol. 34, pp. 16 183–16 195, 2021
2021
Earlier work this paper cites.
S. Reddy, W. Rogers, V.-P. Makinen, E. Coiera, P. Brown, M. Wenzel, E. Weicken, S. Ansari, P. Mathur, A. Casey et al. , “Evaluation framework to guide implementation of ai systems into healthcare settings,” BMJ health & care informatics , vol. 28, no. 1, 2021
2021
Earlier work this paper cites.
S. Basu, A. Garimella, W. Han, and A. Dennis, “Human decision making in ai augmented systems: Evidence from the initial coin offering market,” 2021
2021
Cited alongside, same era.
Z. Wu, D. Ji, K. Yu, X. Zeng, D. Wu, and M. Shidujaman, “Ai creativity and the human-ai co-creation model,” in Human-Computer Interaction. Theory, Methods and Tools: Thematic Area, HCI 2021, Held as Part of the 23rd HCI International Conference, HCII 2021, Virtual Event, July 24–29, 2021, Proceedings, Part I 23 . Springer, 2021, pp. 171–190
2021
Cited alongside, same era.
2021
Cited alongside, same era.
P. Mikalef and M. Gupta, “Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance,” Information & Management , vol. 58, no. 3, p. 103434, 2021
G. V. Aher, R. I. Arriaga, and A. T. Kalai, “Using large language models to simulate multiple humans and replicate human subject studies,” in International Conference on Machine Learning . PMLR, 2023, pp. 337–371
2023
Later among the works it cites.
M. P. Verheijden and M. Funk, “Collaborative diffusion: Boosting designerly co-creation with generative ai,” in Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems , 2023, pp. 1–8
2023
Later among the works it cites.
A. I. Hauptman, B. G. Schelble, N. J. McNeese, and K. C. Madathil, “Adapt and overcome: Perceptions of adaptive autonomous agents for human-ai teaming,” Computers in Human Behavior , vol. 138, p. 107451, 2023
2023
Later among the works it cites.
J. A. Oravec, “Artificial intelligence implications for academic cheating: Expanding the dimensions of responsible human-ai collaboration with chatgpt,” Journal of Interactive Learning Research , vol. 34, no. 2, pp. 213–237, 2023
2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2021
Cited alongside, same era.
X. Yin, J. Huang, W. He, W. Guo, H. Yu, and L. Cui, “Group task allocation approach for heterogeneous software crowdsourcing tasks,” Peer-to-Peer Networking and Applications , vol. 14, pp. 1736–1747, 2021
2021
Cited alongside, same era.
M. Hou, “Enabling trust in autonomous human-machine teaming,” in 2021 IEEE International Conference on Autonomous Systems (ICAS) . IEEE, 2021, pp. 1–1
2021
Cited alongside, same era.
M. Suh, E. Youngblom, M. Terry, and C. J. Cai, “Ai as social glue: uncovering the roles of deep generative ai during social music composition,” in Proceedings of the 2021 CHI conference on human factors in computing systems , 2021, pp. 1–11
2021
Cited alongside, same era.
E. T. Esfahani, B. He, C.-H. Chu, Y. Liu, R. Rai, and G. Ameta, “Special issue: Symbiotic human-ai partnership for next generation factories,” Journal of Computing and Information Science in Engineering , vol. 22, no. 5, 2022
2022
Cited alongside, same era.
K. Holstein and V. Aleven, “Designing for human–ai complementarity in k-12 education,” AI Magazine , vol. 43, no. 2, pp. 239–248, 2022
2022
Cited alongside, same era.
2022
Cited alongside, same era.
K. G. Van Leeuwen, M. de Rooij, S. Schalekamp, B. van Ginneken, and M. J. Rutten, “How does artificial intelligence in radiology improve efficiency and health outcomes?” Pediatric Radiology , pp. 1–7, 2022
2022
Cited alongside, same era.
F. M. Calisto, C. Santiago, N. Nunes, and J. C. Nascimento, “Breastscreening-ai: Evaluating medical intelligent agents for human-ai interactions,” Artificial Intelligence in Medicine , vol. 127, p. 102285, 2022
2022
Cited alongside, same era.
S. Alon-Barkat and M. Busuioc, “Human–ai interactions in public sector decision making:“automation bias” and “selective adherence” to algorithmic advice,” Journal of Public Administration Research and Theory , vol. 33, no. 1, pp. 153–169, 2023
2023
Later among the works it cites.
F. Fischer, “Future collaboration between humans and ai: “i strive to make you feel good,” says my ai colleague in 2030,” in Work and AI 2030: Challenges and Strategies for Tomorrow’s Work . Springer, 2023, pp. 21–28
2023
Later among the works it cites.
E. M. Lase and F. Nkosi, “Human-centric ai: Understanding and enhancing collaboration between humans and intelligent systems,” Algorithm Asynchronous , vol. 1, no. 1, pp. 33–40, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
G. C. Saha, S. Kumar, A. Kumar, H. Saha, T. Lakshmi, and N. Bhat, “Human-ai collaboration: Exploring interfaces for interactive machine learning,” Tuijin Jishu/Journal of Propulsion Technology , vol. 44, no. 2, p. 2023, 2023
2023
Later among the works it cites.
L. Fabri, B. Häckel, A. M. Oberländer, M. Rieg, and A. Stohr, “Disentangling human-ai hybrids,” Business & information systems engineering , pp. 1–19, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
M. F. Schneider, M. E. Miller, and J. McGuirl, “Assessing quality goal rankings as a method for communicating operator intent,” Journal of Cognitive Engineering and Decision Making , vol. 17, no. 1, pp. 26–48, 2023
2023
Later among the works it cites.
U. Murugesan, P. Subramanian, S. Srivastava, and A. Dwivedi, “A study of artificial intelligence impacts on human resource digitalization in industry 4.0,” Decision Analytics Journal , p. 100249, 2023
2023
Later among the works it cites.
K. Inkpen, S. Chappidi, K. Mallari, B. Nushi, D. Ramesh, P. Michelucci, V. Mandava, L. H. Vepřek, and G. Quinn, “Advancing human-ai complementarity: The impact of user expertise and algorithmic tuning on joint decision making,” ACM Transactions on Computer-Human Interaction , vol. 30, no. 5, pp. 1–29, 2023
2023
Later among the works it cites.
S. Barocas, M. Hardt, and A. Narayanan, Fairness and machine learning: Limitations and opportunities . MIT press, 2023
2023
Later among the works it cites.
D. Zheng, X. He, and J. Jing, “Overview of artificial intelligence in breast cancer medical imaging,” Journal of Clinical Medicine , vol. 12, no. 2, p. 419, 2023
2023
Later among the works it cites.
A. Kaboudan and W. Salah Eldin, “Ai-driven medical imaging platform: Advancements in image analysis and healthcare diagnosis,” Journal of the ACS Advances in Computer Science , vol. 14, no. 1, 2023
2023
Later among the works it cites.
R. Jain, “Role of artificial intelligence in banking and finance,” Journal of Management and Science , vol. 13, no. 3, pp. 1–4, 2023
2023
Later among the works it cites.
D. Ifenthaler and C. Schumacher, “Reciprocal issues of artificial and human intelligence in education,” pp. 1–6, 2023
2023
Later among the works it cites.
H. Ji, I. Han, and Y. Ko, “A systematic review of conversational ai in language education: Focusing on the collaboration with human teachers,” Journal of Research on Technology in Education , vol. 55, no. 1, pp. 48–63, 2023
2023
Later among the works it cites.
O. Fadiya, “Development of a student-centred manual using appreciative inquiry,” International Journal of Quality and Service Sciences , no. ahead-of-print, 2023
2023
Later among the works it cites.
K. S. Kalyan, “A survey of gpt-3 family large language models including chatgpt and gpt-4,” Natural Language Processing Journal , p. 100048, 2023
2023
Later among the works it cites.
M. Binz and E. Schulz, “Using cognitive psychology to understand gpt-3,” Proceedings of the National Academy of Sciences , vol. 120, no. 6, p. e2218523120, 2023
2023
Later among the works it cites.
T. Webb, K. J. Holyoak, and H. Lu, “Emergent analogical reasoning in large language models,” Nature Human Behaviour , vol. 7, no. 9, pp. 1526–1541, 2023
2023
Later among the works it cites.
M. R. Douglas, “Large language models,” arXiv preprint arXiv:2307.05782 , 2023
2023
Later among the works it cites.
Y. Liu, T. Han, S. Ma, J. Zhang, Y. Yang, J. Tian, H. He, A. Li, M. He, Z. Liu et al. , “Summary of chatgpt-related research and perspective towards the future of large language models,” Meta-Radiology , p. 100017, 2023
2023
Later among the works it cites.
Z. Yin, Q. Sun, Q. Guo, J. Wu, X. Qiu, and X. Huang, “Do large language models know what they don’t know?” 2023
2023
Later among the works it cites.
Y. K. Dwivedi, N. Kshetri, L. Hughes, E. L. Slade, A. Jeyaraj, A. K. Kar, A. M. Baabdullah, A. Koohang, V. Raghavan, M. Ahuja et al. , ““so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy,” International Journal of Information Management , vol. 71, p. 102642, 2023
2023
Later among the works it cites.
H. H. Jiang, L. Brown, J. Cheng, M. Khan, A. Gupta, D. Workman, A. Hanna, J. Flowers, and T. Gebru, “Ai art and its impact on artists,” in Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society , 2023, pp. 363–374
2023
Later among the works it cites.
V. F. He, Y. R. Shrestha, P. Puranam, and E. Miron-Spektor, “Searching together: A theory of human-ai co-creativity,” 2023
2023
Later among the works it cites.
Z. Epstein, A. Hertzmann, I. of Human Creativity, M. Akten, H. Farid, J. Fjeld, M. R. Frank, M. Groh, L. Herman, N. Leach et al. , “Art and the science of generative ai,” Science , vol. 380, no. 6650, pp. 1110–1111, 2023
2023
Later among the works it cites.
T. Woelfle, J. Hirt, P. Janiaud, L. Kappos, J. P. Ioannidis, and L. G. Hemkens, “Benchmarking human–ai collaboration for common evidence appraisal tools,” Journal of Clinical Epidemiology , vol. 175, p. 111533, 2024
2024
Closest in time.
G. Schwalbe and B. Finzel, “A comprehensive taxonomy for explainable artificial intelligence: a systematic survey of surveys on methods and concepts,” Data Mining and Knowledge Discovery , vol. 38, no. 5, pp. 3043–3101, 2024
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
N. Farič, S. Hinder, R. Williams, R. Ramaesh, M. O. Bernabeu, E. van Beek, and K. Cresswell, “Early experiences of integrating an artificial intelligence-based diagnostic decision support system into radiology settings: a qualitative study,” Journal of the American Medical Informatics Association , vol. 31, no. 1, pp. 24–34, 2024
2024
Closest in time.
J. Yik, K. V. den Berghe, D. den Blanken, Y. Bouhadjar, M. Fabre, P. Hueber, D. Kleyko, and et al., “Neurobench: A framework for benchmarking neuromorphic computing algorithms and systems,” 2024
2024
Closest in time.
S. Sachan, F. Almaghrabi, J.-B. Yang, and D.-L. Xu, “Human-ai collaboration to mitigate decision noise in financial underwriting: A study on fintech innovation in a lending firm,” International Review of Financial Analysis , p. 103149, 2024
2024
Closest in time.
2024
Closest in time.
K. Nikolopoulou, “Generative artificial intelligence in higher education: Exploring ways of harnessing pedagogical practices with the assistance of chatgpt,” International Journal of Changes in Education , 2024
2024
Closest in time.
S. Nasir, R. A. Khan, and S. Bai, “Ethical framework for harnessing the power of ai in healthcare and beyond,” IEEE Access , vol. 12, pp. 31 014–31 035, 2024
2024
Closest in time.
A. P. Brady, B. Allen, J. Chong, E. Kotter, N. Kottler, J. Mongan, L. Oakden-Rayner, D. P. Dos Santos, A. Tang, C. Wald et al. , “Developing, purchasing, implementing and monitoring ai tools in radiology: practical considerations. a multi-society statement from the acr, car, esr, ranzcr & rsna,” Insights into Imaging , vol. 15, no. 1, p. 16, 2024
2024
Closest in time.
2024
Closest in time.
I. Hein, J. Cecil, and E. Lermer, “Acceptance and motivational effect of ai-driven feedback in the workplace: an experimental study with direct replication,” Frontiers in Organizational Psychology , vol. 2, p. 1468907, 2024
2024
Closest in time.
A. Atadoga, O. C. Obi, S. Onwusinkwue, S. O. Dawodu, F. Osasona, A. I. Daraojimba et al. , “Ai’s evolving impact in us banking: An insightful review,” International Journal of Science and Research Archive , vol. 11, no. 1, pp. 904–922, 2024
2024
Closest in time.