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Promoting Human Competences by Appropriate Modes of Interaction for Human-Centered-AI

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13336))

Abstract

There is an ongoing discussion about human-centered AI (HCAI) that emphasizes the value of including humans in the loop. We focus on types of HCAI in the context of machine learning that synergistically combine the complementary strengths of humans and AI and seek to develop competencies and capabilities of both parts. The development of human competencies is a largely neglected aspect compared to criteria such as fairness, trust, or accountability. Based on early discussions about the role of humans in the use of expert systems, the current HCAI discourse, and a literature review, we identify 10 modes of interaction that represent a way of interacting with AI that has the potential to support the development of human competencies relevant to the domain itself, but also to its context and to the use of technologies.

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References

  • Beede, E., et al.: A human-centered evaluation of a deep learning system deployed in clinics for the detection of diabetic retinopathy. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–12 (2020). https://doi.org/10.1145/3313831.3376718

  • Bond, R.R., Mulvenna, M., Wang, H.: Human centered artificial intelligence: weaving UX into algorithmic decision making. In: RoCHI, pp. 2–9 (2019)

    Google Scholar 

  • 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)

    Google Scholar 

  • Chromik, M., Butz, A.: Human-XAI interaction: a review and design principles for explanation user interfaces. In: Ardito, C., et al. (eds.) INTERACT 2021. LNCS, vol. 12933, pp. 619–640. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-85616-8_36

    Chapter  Google Scholar 

  • Cirqueira, D., Helfert, M., Bezbradica, M.: Towards design principles for user-centric explainable AI in fraud detection. In: Degen, H., Ntoa, S. (eds.) HCII 2021. LNCS (LNAI), vol. 12797, pp. 21–40. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-77772-2_2

    Chapter  Google Scholar 

  • Croskerry, P.: Cognitive forcing strategies in clinical decisionmaking. Ann. Emerg. Med. 41(1), 110–120 (2003). https://doi.org/10.1067/mem.2003.22

    Article  Google Scholar 

  • Crowley, J., et al.: Toward AI systems that augment and empower humans by understanding us, our society and the world around us. Report of 761758 EU Project HumaneAI, vol. 761758, pp. 1–32 (2019)

    Google Scholar 

  • 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 (2019)

    Google Scholar 

  • Ehsan, U., Liao, Q. V., Muller, M., Riedl, M.O., Weisz, J.D.: Expanding explainability: towards social transparency in AI systems. arXiv:2101.04719 [Cs], https://doi.org/10.1145/3411764.3445188 (2021)

  • Ehsan, U., et al.: The who in explainable AI: how AI background shapes perceptions of AI explanations. arXiv:2107.13509 [Cs] (2021)

  • Endsley, M.R.: From here to autonomy: lessons learned from human-automation research. Hum. Factors J. Hum. Factors Ergon. Soc. 59(1), 5–27 (2017). https://doi.org/10.1177/0018720816681350

    Article  Google Scholar 

  • Fischer, G.: Domain-oriented design environments. Autom. Softw. Eng. 1(2), 177–203 (1994)

    Article  Google Scholar 

  • Fischer, G.: End-user development: empowering stakeholders with artificial intelligence, meta-design, and cultures of participation. In: Fogli, D., Tetteroo, D., Barricelli, B.R., Borsci, S., Markopoulos, P., Papadopoulos, G.A. (eds.) IS-EUD 2021. LNCS, vol. 12724, pp. 3–16. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-79840-6_1

    Chapter  Google Scholar 

  • Herrmann, T.: Rationalität und Irrationalität in der Mensch-Computer-Interaktion (Master Thesis). University of Bonn (1983). https://doi.org/10.13140/RG.2.2.35273.21607

  • Herrmann, T., Ackermann, M.S., Goggins, S.P., Stary, C., Prilla, M.: Designing health care that works – socio-technical conclusions. In: Designing Healthcare That Works. A Socio-technical Approach, S. 187–203. Academic Press (2017)

    Google Scholar 

  • Herrmann, T., Jahnke, I., Nolte, A.: A problem-based approach to the advancement of heuristics for socio-technical evaluation. Behav. Inf. Technol., pp. 1–23 (2021). https://doi.org/10.1080/0144929X.2021.1972157

  • Herrmann, T., Just, K.: Experts’ systems instead of expert systems. AI Soc. 9(4), 321–355 (1995)

    Article  Google Scholar 

  • Herrmann, T., Pfeiffer, S.: Keeping the organization in the loop: a socio-technical extension of human-centered artificial intelligence (2022). https://doi.org/10.1007/s00146-022-01391-5

  • Jarrahi, M.H.: Artificial intelligence and the future of work: human-AI symbiosis in organizational decision making. Bus. Horiz. 61(4), 577–586 (2018)

    Article  Google Scholar 

  • Johnson, A.W., Duda, K.R., Sheridan, T.B., Oman, C.M.: A closed-loop model of operator visual attention, situation awareness, and performance across automation mode transitions. Hum. Factors J. Hum. Factors Ergon. Soc. 59(2), 229–241 (2017). https://doi.org/10.1177/0018720816665759

    Article  Google Scholar 

  • Kaluarachchi, T., Reis, A., Nanayakkara, S.: A review of recent deep learning approaches in human-centered machine learning. Sensors 21(7), 2514 (2021). https://doi.org/10.3390/s21072514

    Article  Google Scholar 

  • Kamar, E.: Directions in hybrid intelligence: complementing AI systems with human intelligence. In: IJCAI, pp. 4070–4073 (2016)

    Google Scholar 

  • Lieberman, H., Paterno, F., Klann, M., Wulf, V.: End-user development: an emerging paradigm. In: End User Development, pp. 1–8 (2006). https://doi.org/10.1007/1-4020-5386-X_1

  • Longo, L., Goebel, R., Lecue, F., Kieseberg, P., Holzinger, A.: Explainable artificial intelligence: concepts, applications, research challenges and visions. In: Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds.) CD-MAKE 2020. LNCS, vol. 12279, pp. 1–16. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-57321-8_1

    Chapter  Google Scholar 

  • Margetis, G., Ntoa, S., Antona, M., Stephanidis, C.: Human‐centered design of artificial intelligence. In: Salvendy, G., Karwowski, W. (eds.), Handbook of Human Factors and Ergonomics, 1st edn., pp. 1085–1106. Wiley (2021). https://doi.org/10.1002/9781119636113.ch42

  • Prilla, M., Degeling, M., Herrmann, T.: Collaborative reflection at work: supporting informal learning at a healthcare workplace. In: Proceedings of the 17th ACM International Conference on Supporting Group Work, pp. 55–64 (2012). https://doi.org/10.1145/2389176.2389185

  • Rakova, B., Yang, J., Cramer, H., Chowdhury, R.: Where responsible AI meets reality: practitioner perspectives on enablers for shifting organizational practices. In: Proceedings of the ACM on Human-Computer Interaction, vol. 5, no. CSCW1, pp. 1–23 (2021)

    Google Scholar 

  • Schmidt, A.: Implicit human computer interaction through context. Pers. Ubiquitous Comput. 4(2/3), 191–199 (2000). https://doi.org/10.1007/BF01324126

    Article  Google Scholar 

  • Schmidt, A., Herrmann, T.: Intervention user interfaces: a new interaction paradigm for automated systems. Interactions 24(5), 40–45 (2017)

    Article  Google Scholar 

  • Serafini, L., et al.: On some foundational aspects of human-centered artificial intelligence. arXiv:2112.14480 [Cs] (2021)

  • Shergadwala, M.N., El-Nasr, M.S.: Human-centric design requirements and challenges for enabling human-AI Interaction in engineering design: an interview study. In: International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, vol. 85420, p. V006T06A054. American Society of Mechanical Engineers (2021)

    Google Scholar 

  • Shin, D.: The effects of explainability and causability on perception, trust, and acceptance: implications for explainable AI. Int. J. Hum. Comput. Stud. 146, 102551 (2021). https://doi.org/10.1016/j.ijhcs.2020.102551

    Article  Google Scholar 

  • Shneiderman, B.: A taxonomy and rule base for the selection of interaction styles. In: Readings in Human–Computer Interaction, pp. 401–410 (1995). https://doi.org/10.1016/B978-0-08-051574-8.50042-X

  • 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

    Article  Google Scholar 

  • Shneiderman, B.: Human-Centered AI. Oxford University Press, Oxford (2022)

    Book  Google Scholar 

  • Valverde, R.: Principles of Human Computer Interaction Design: HCI Design. LAP Lambert Academic Publishing, Sunnyvale (2011)

    Google Scholar 

  • Wexler, J., Pushkarna, M., Bolukbasi, T., Wattenberg, M., Viegas, F., Wilson, J.: The what-if tool: interactive probing of machine learning models. IEEE Trans. Visual Comput. Graph. 26, 56–65 (2019). https://doi.org/10.1109/TVCG.2019.2934619

    Article  Google Scholar 

  • Wilkens, U., Sprafke, N.: Micro-variables of dynamic capabilities and how they come into effect – exploring firm-specificity and cross-firm commonalities. Manag. Int. 23(4), 30–49 (2019). https://doi.org/10.7202/1066068ar

    Article  Google Scholar 

  • Wright, A.P., et al.: A comparative analysis of industry human-AI interaction guidelines. arXiv:2010.11761 [Cs] (2020)

  • Xu, W., Dainoff, M.J., Ge, L., Gao, Z.: Transitioning to human interaction with AI systems: new challenges and opportunities for HCI professionals to enable human-centered AI. arXiv:2105.05424 [Cs] (2021)

  • Yang, Q., Steinfeld, A., Rosé, C., Zimmerman, J.: Re-examining whether, why, and how human-AI interaction is uniquely difficult to design. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–13 (2020). https://doi.org/10.1145/3313831.3376301

  • Yang, Y., Kandogan, E., Li, Y., Sen, P., Lasecki, W.S.: A study on interaction in human-in-the-loop machine learning for text analytics, Los Angeles, vol. 7 (2019)

    Google Scholar 

  • Zanzotto, F.M.: Viewpoint: human-in-the-loop artificial intelligence. J. Artif. Intell. Res. 64, 243–252 (2019). https://doi.org/10.1613/jair.1.11345

    Article  MathSciNet  Google Scholar 

  • Zhang, Z.T., Liu, Y., Hussmann, H.: Forward reasoning decision support: toward a more complete view of the human-AI interaction design space. In: CHItaly 2021: 14th Biannual Conference of the Italian SIGCHI Chapter, pp. 1–5 (2021). https://doi.org/10.1145/3464385.3464696

  • Zhou, L., et al.: Intelligence augmentation: towards building human-machine symbiotic relationship. AIS Trans. Hum.-Comput. Interact. 13(2), 243–264 (2021). https://doi.org/10.17705/1thci.00149

    Article  Google Scholar 

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Funding

This work was supported by the project Humaine (Human centered AI Network) that is funded by the Federal Ministry of Education and Research (BMBF), Germany within the “Zukunft der Wertschöpfung – Forschung zu Produktion, Dienstleistung und Arbeit” Program (funding-number: 02L19C200).

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Correspondence to Thomas Herrmann .

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Herrmann, T. (2022). Promoting Human Competences by Appropriate Modes of Interaction for Human-Centered-AI. In: Degen, H., Ntoa, S. (eds) Artificial Intelligence in HCI. HCII 2022. Lecture Notes in Computer Science(), vol 13336. Springer, Cham. https://doi.org/10.1007/978-3-031-05643-7_3

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  • DOI: https://doi.org/10.1007/978-3-031-05643-7_3

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