Abstract
Human-centered AI (HCAI) refers to guidelines or principles that aim on ethically oriented design of systems. We compare HCAI-guidelines with principles of socio-technical systems that emerged in the context of conventional information technology. The comparison leads to a revision of socio-technical heuristics by including aspects of AI-usage. The comparison reveals that continuous evolution is a basic characteristic of socio-technical systems, and that human oversight or interventions and the subsequent appropriation of AI-systems lead to continuous adaptation and re-design of the systems, if autonomy is collaboratively exercised. From a socio-technical point of view, the crucial requirement of transparency has not only to be fulfilled with technical features, but also by contributions of the whole system including human actors. It will be promising for using AI, if not only technical features, but organizational and social practices are socio-technically designed in a way that compensates shortcomings of AI.
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References
Shneiderman, B.: Human-Centered AI. Oxford University Press, Oxford (2022)
Dellermann, D., Calma, A., Lipusch, N., Weber, T., Weigel, S., Ebel, P.: The future of human-AI collaboration: a taxonomy of design knowledge for hybrid intelligence systems. In: Proceedings of the 52nd Hawaii International Conference on System Sciences (HICSS) (2019)
Garibay, O.O., et al.: Six human-centered artificial intelligence grand challenges. Int. J. Hum.-Compute. Interact. 39(3), 391–437 (2023). https://doi.org/10.1080/10447318.2022.2153320
Dwivedi, Y.K., et al.: Opinion paper: ‘so what if ChatGPT wrote it?’ Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. Int. J. Inf. Manag. 71, 102642 (2023). https://doi.org/10.1016/j.ijinfomgt.2023.102642
Bingley, W.J., et al.: Where is the human in human-centered AI? Insights from developer priorities and user experiences. Comput. Hum. Behav. 141, 107617 (2023). https://doi.org/10.1016/j.chb.2022.107617
European Commission, Directorate-General for Communications Networks, Content and Technology, Ethics guidelines for trustworthy AI (2019). https://data.europa.eu/doi/10.2759/346720. Accessed 23 May 2021
Weisz, J.D., Muller, M., He, J., Houde, S.: Toward general design principles for generative AI applications. arXiv, 13 January 2023. http://arxiv.org/abs/2301.05578. Accessed 26 Oct 2023.
Cherns, A.: Principles of sociotechnical design revisited. Hum. Relat. 40(3), 153–162 (1987)
Cherns, A.: The principles of sociotechnical design. Hum. Relat. 29(8), 783–792 (1976)
Mumford, E.: Designing Human Systems for New Technology: The ETHICS Method. Manchester Business School (1983). https://books.google.de/books?id=JTjxIwAACAAJ
Clegg, C.W.: Sociotechnical principles for system design. Appl. Ergon. 31(5), 463–477 (2000). https://doi.org/10.1016/S0003-6870(00)00009-0
Herrmann, T., Jahnke, I., Nolte, A.: A problem-based approach to the advancement of heuristics for socio-technical evaluation. Behav. Inf. Technol. 41(14), 3087–3109 (2022). https://doi.org/10.1080/0144929X.2021.1972157
Herrmann, T.: Promoting human competences by appropriate modes of interaction for human-centered-AI. In: Degen, H., Ntoa, S. (eds.) HCII 2022. LNCS, vol. 13336, pp. 35–50. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-05643-7_3
Chatila, R., Havens, J.C.: The IEEE global initiative on ethics of autonomous and intelligent systems. In: Aldinhas Ferreira, M.I., Silva Sequeira, J., Virk, G.S., Tokhi, M.O., Kadar, E.E. (eds.) Robotics and Well-Being. ISCASE, vol. 95, pp. 11–16. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-12524-0_2
De Visser, E.J., Pak, R., Shaw, T.H.: From ‘automation’ to ‘autonomy’: the importance of trust repair in human–machine interaction. Ergonomics 61(10), 1409–1427 (2018). https://doi.org/10.1080/00140139.2018.1457725
Jobin, A., Ienca, M., Vayena, E.: The global landscape of AI ethics guidelines. Nat. Mach. Intell. 1(9), 389–399 (2019). https://doi.org/10.1038/s42256-019-0088-2
Fjeld, J., Achten, N., Hilligoss, H., Nagy, A., Srikumar, M.: Principled artificial intelligence: mapping consensus in ethical and rights-based approaches to principles for AI. SSRN J. (2020). https://doi.org/10.2139/ssrn.3518482
Usmani, U.A., Happonen, A., Watada, J.: Human-centered artificial intelligence: designing for user empowerment and ethical considerations. In: 2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA). IEEE, Istanbul, Turkiye, June 2023, pp. 1–7 (2023). https://doi.org/10.1109/HORA58378.2023.10156761
Shneiderman, B.: Bridging the gap between ethics and practice: guidelines for reliable, safe, and trustworthy human-centered ai systems. ACM Trans. Interact. Intell. Syst. 10(4), 1–31 (2020). https://doi.org/10.1145/3419764
Shneiderman, B.: Responsible AI: bridging from ethics to practice. Commun. ACM 64(8), 32–35 (2021). https://doi.org/10.1145/3445973
Hofeditz, L., Mirbabaie, M., Ortmann, M.: Ethical challenges for human–agent interaction in virtual collaboration at work. Int. J. Hum.–Comput. Interact. 1–17 (2023). https://doi.org/10.1080/10447318.2023.2279400
Kieslich, K., Keller, B., Starke, C.: Artificial intelligence ethics by design. Evaluating public perception on the importance of ethical design principles of artificial intelligence. Big Data Soc. 9(1), 205395172210929 (2022). https://doi.org/10.1177/20539517221092956
Díaz-Rodríguez, N., Del Ser, J., Coeckelbergh, M., López De Prado, M., Herrera-Viedma, E., Herrera, F.: Connecting the dots in trustworthy artificial intelligence: from AI principles, ethics, and key requirements to responsible AI systems and regulation. Inf. Fusion 99, 101896 (2023). https://doi.org/10.1016/j.inffus.2023.101896
Georgieva, I., Lazo, C., Timan, T., Van Veenstra, A.F.: From AI ethics principles to data science practice: a reflection and a gap analysis based on recent frameworks and practical experience. AI Ethics 2(4), 697–711 (2022). https://doi.org/10.1007/s43681-021-00127-3
Noble, S.M., Dubljević, V.: Ethics of AI in organizations. In: Human-Centered Artificial Intelligence, pp. 221–239. Elsevier, Amsterdam (2022). https://doi.org/10.1016/B978-0-323-85648-5.00019-0
Reinhardt, K.: Trust and trustworthiness in AI ethics. AI Ethics 3(3), 735–744 (2023). https://doi.org/10.1007/s43681-022-00200-5
Amershi, S., Cakmak, M., Knox, W.B., Kulesza, T.: Power to the people: the role of humans in interactive machine learning. AI Mag. 35(4), 105–120 (2014)
Jarrahi, M.H.: Artificial intelligence and the future of work: human-AI symbiosis in organizational decision making. Bus. Horiz. 61(4), 577–586 (2018)
Fogliato, R., et al.: Who goes first? Influences of human-AI workflow on decision making in clinical imaging. arXiv, 19 May 2022. http://arxiv.org/abs/2205.09696. Accessed 03 June 2022
Schmidt, A., Herrmann, T.: Intervention user interfaces: a new interaction paradigm for automated systems. Interactions 24(5), 40–45 (2017)
Rakova, B., Yang, J., Cramer, H., Chowdhury, R.: Where responsible AI meets reality: practitioner perspectives on enablers for shifting organizational practices. Proc. ACM Hum.-Comput. Interact. 5(CSCW1), 1–23 (2021)
Cai, C.J., et al.: Human-centered tools for coping with imperfect algorithms during medical decision-making. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2019)
Cai, C.J., Winter, S., Steiner, D., Wilcox, L., Terry, M.: ‘Hello AI’: uncovering the onboarding needs of medical practitioners for human-AI collaborative decision-making. Proc. ACM Hum.-Comput. Interact. 3(CSCW), 1–24 (2019). https://doi.org/10.1145/3359206
Schneider, J., Meske, C., Kuss, P.: Foundation models: a new paradigm for artificial intelligence. Bus. Inf. Syst. Eng. (2024). https://doi.org/10.1007/s12599-024-00851-0
Herrmann, T., Pfeiffer, S.: Keeping the organization in the loop as a general concept for human-centered AI: the example of medical imaging. In: Proceedings of the 56th Hawaii International Conference on System Sciences (HICSS), pp. 5272–5281 (2023)
Ackermann, M.S., Goggins, S.P., Herrmann, T., Prilla, M., Stary, C.: Designing Healthcare That Works – A Socio-technical Approach. Academic Press, United Kingdom, United States (2018)
Okamura, K., Yamada, S.: Adaptive trust calibration for human-AI collaboration. PLoS ONE 15(2), e0229132 (2020). https://doi.org/10.1371/journal.pone.0229132
Herrmann, T., Pfeiffer, S.: Keeping the organization in the loop: a socio-technical extension of human-centered artificial intelligence. AI Soc. 38, 1523–1542 (2023). https://doi.org/10.1007/s00146-022-01391-5
Herrmann, T., Lentzsch, C., Degeling, M.: Intervention and EUD. In: Malizia, A., Valtolina, S., Morch, A., Serrano, A., Stratton, A. (eds.) IS-EUD 2019. LNCS, vol. 11553, pp. 67–82. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-24781-2_5
Herrmann, T.: Collaborative appropriation of AI in the context of interacting with AI. In: Degen, H., Ntoa, S. (eds.) HCII 2023. LNCS, vol. 14051, pp. 249–260. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-35894-4_18
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Herrmann, T. (2024). Comparing Socio-technical Design Principles with Guidelines for Human-Centered AI. In: Degen, H., Ntoa, S. (eds) Artificial Intelligence in HCI. HCII 2024. Lecture Notes in Computer Science(), vol 14735. Springer, Cham. https://doi.org/10.1007/978-3-031-60611-3_5
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