Volume 8, Issue 4 (2-2022)
Human Information Interaction 2022, 8(4): 1-14 |
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Abstract: (1907 Views)
Background: Semantic technologies (STs) have made machine reasoning possible by providing intelligent data management methods. This capability has created new forms of interaction between humans and STs, which is called "semantic interaction." The increasing spread of this form of interaction in daily life reveals the need to identify the factors affecting it and introduce the requirements of a synergistic interaction, which in this study is interpreted as a model of symbiosis.
Purpose: The main purpose of this study is to investigate what, why, and how human-ST symbiosis occurs in the form of a symbiosis model. Providing such a model could be valuable in developing active strategies in the face of intelligent technologies.
Methodology: The study introduces actor-networks of human symbiosis based on the actor-network methodology. Data was collected through in-depth interviews with eight managers, experts, and users in the Computer Research Centers of Islamic Sciences (CRCIS) and examined using the actor network method. All phases of data collection, implementations, coding and analysis were done under NVivo software.
Findings: In the human-ST symbiosis, beside human eleven other actors: Semantic products, context, infrastructure, data, knowledge, social media, Web, scientific centers, organization, AI and ontology are identified. Their interaction establishes seven dynamic actors-networks of symbiosis: Product design and development, use, leadership and management, data, knowledge management, training and contextual conditions.
Conclusion: Semantic products alongside human beings are independent, autonomous, and self-aware actors who are able to go beyond mere mediation of change and govern social change in the Human-ST symbiosis. In such circumstances, man, as the creator and maintainer of the semantic product, in addition to strengthening the technical capabilities in the creation of the product, must entrust to the product the things that the product is able to do.
Purpose: The main purpose of this study is to investigate what, why, and how human-ST symbiosis occurs in the form of a symbiosis model. Providing such a model could be valuable in developing active strategies in the face of intelligent technologies.
Methodology: The study introduces actor-networks of human symbiosis based on the actor-network methodology. Data was collected through in-depth interviews with eight managers, experts, and users in the Computer Research Centers of Islamic Sciences (CRCIS) and examined using the actor network method. All phases of data collection, implementations, coding and analysis were done under NVivo software.
Findings: In the human-ST symbiosis, beside human eleven other actors: Semantic products, context, infrastructure, data, knowledge, social media, Web, scientific centers, organization, AI and ontology are identified. Their interaction establishes seven dynamic actors-networks of symbiosis: Product design and development, use, leadership and management, data, knowledge management, training and contextual conditions.
Conclusion: Semantic products alongside human beings are independent, autonomous, and self-aware actors who are able to go beyond mere mediation of change and govern social change in the Human-ST symbiosis. In such circumstances, man, as the creator and maintainer of the semantic product, in addition to strengthening the technical capabilities in the creation of the product, must entrust to the product the things that the product is able to do.
Keywords: Semantic technology, Human Technology Interaction, Human ST Symbiosis, Actor-Network theory.
References
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29. Callon, M. (1984). Some elements of a sociology of translation: domestication of the scallops and the fishermen of St Brieuc Bay. The sociological review, 32(1_suppl), 196-233.
31. Cvetinovic, M., Nedovic-Budic, Z., & Bolay, J.-C. (2017). Decoding urban development dynamics through actor-network methodological approach. Geoforum, 82, 141-157. doi:
33. Douša, P. (2011). Using Actor-Network Theory to Enhance the Mediating Activities of Grassroots Support Organizations.
34. Foster, I. (2008). Human-Machine Symbiosis, 50 Years On (Vol. 16).
35. Gane, N., & Haraway, D. (2006). When we have never been human, what is to be done? Interview with Donna Haraway. Theory Culture & Society, 23(7-8), 135-+. doi:10.1177/0263276406069228
37. Gill, K. S. (2012). Human machine symbiosis: The foundations of human-centred systems design: Springer Science & Business Media.
38. Grigsby, S. (2018). Artificial Intelligence for Advanced Human-Machine Symbiosis. In (pp. 255-266).
40. Hashemian, M. H., & Anvari, M. (1397). Implications of Actor-network theory in cultural policy-making: The interaction of technology and human in policy-making. Religion and cultural politics, 1397(10), 37-64.
41. Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons. doi:
43. Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-Al symbiosis in organizational decision making. Business Horizons, 61(4), 577-586. doi:10.1016/j.bushor.2018.03.007
45. Kohlhase, A., & Kohlhase, M. (2008). Semantic knowledge management for education. Proceedings of the IEEE, 96(6), 970-989. doi:10.1109/jproc.2008.921606
47. Latour, B. (2005). Reassembling the Social-An Introduction to Actor Network Theory: Oxford university press.
49. Lee, J., & Seppelt, B. (2009). Human Factors in Automation Design. In (pp. 417-436).
51. Licklider, J. C. R. (1960). Man-Computer Symbiosis. IRE Transactions on Human Factors in Electronics, HFE-1(1), 4-11. doi:10.1109/THFE2.1960.4503259
53. Nabipour, I., & Assadi, M. (2016). The technological singularity and exponential medicine. BPUMS, 18(6), 1287-1298. doi:10.7508/ismj.1394.06.018
54. Parry, K., Cohen, M., & Bhattacharya, S. (2016). Rise of the Machines: A Critical Consideration of Automated Leadership Decision Making in Organizations. Group & Organization Management, 41(5), 571-594. doi:10.1177/1059601116643442
56. Pope, C., Halford, S., Turnbull, J., & Prichard, J. (2014). Cyborg practices: Call-handlers and computerised decision support systems in urgent and emergency care. Health Informatics Journal, 20(2), 118-126. doi:10.1177/1460458213486470
59. Rouse,M.(2017).DEFINITIONsemantictechnology.Retrievedfrom https://searchdatamanagement.techtarget.com/definition/semantic-technology
60. Sandini, G., Mohan, V., Sciutti, A., & Morasso, P. (2018). Social Cognition for Human-Robot Symbiosis-Challenges and Building Blocks. Frontiers in Neurorobotics, 12, 19. doi:10.3389/fnbot.2018.00034
63. Seppelt, B., & Lee, J. (2012). Human Factors and Ergonomics in Automation Design. In (pp. p. 1615-1642).
65. Tatnall, A., & Gilding, A. (1999). Actor-Network Theory and Information Systems Research.
66. Williams, I. (2020). Contemporary applications of actor network theory. Singapore: Palgrave Macmillan.
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