-------- Forwarded Message -------- Subject: [wkwi] pre-ECIS 2019 Workshop "Thinking machines and design: A time to reconsider" Date: Tue, 12 Mar 2019 11:05:13 +0000 From: Stefan Seidel Stefan.Seidel@uni.li Reply-To: Stefan Seidel Stefan.Seidel@uni.li To: wkwi@listserv.dfn.de wkwi@listserv.dfn.de CC: Kalle Lyytinen kjl13@case.edu, rikard.lindgren@ait.gu.se rikard.lindgren@ait.gu.se, Aron Lindberg alindber@stevens.edu, tuure@tuunanen.fi tuure@tuunanen.fi

We would like to invite you to a workshop on thinking machines and design on June 10 or 11 in Stockholm, Sweden (exact date time TBD):

http://ecis2019.eu/programme/workshops-and-tutorials/thinking-machines-and-d...

Innovators and systems designers in a wide range of areas, such as architecture, semiconductor chip design, video game development, industrial design, and construction, increasingly use smarter and largely autonomous tools to generate better designs. Such tools are increasingly acquiring autonomy and learning capabilities, thus prompting interactions between humans and machines to change as the latter gain more intelligence  (Seidel, Berente, Lindberg, et al. 2018).

Design resulting from such interactions are often novel and useful, i.e. innovative (Amabile 1996; Leonard-Barton 1998). Autonomous tools deploy software that undergird artificial intelligence (AI) methods such as machine learning, pattern recognition, meta-heuristics, and evolutionary algorithms to generate design artifacts beyond the capabilities of most—or maybe any—humans. Such tools allow designers to algorithmically generate a solution space based on certain parameters, within which outcomes are generated. The autonomous generation of design outcomes is also known as procedural generation (Hendrikx et al. 2011), procedural modeling (Mnih et al. 2015), and even computational creativity (Liapis et al. 2014).

Using autonomous design tools fundamentally revamps the process of innovation and design. Tasks that were formerly conducted by human designers are now conducted by tools. Designers must understand how tools can be used, and they must be able to evaluate the outcomes that autonomous tools generate. Moreover, tools must be developed and adjusted to match the designers’ understanding of the overall innovation process, and these decisions with regards to the tool become part of the overall design endeavor (Seidel, Berente, Martinez, et al. 2018.)

We know little about how AI-based methods will change design practices, necessary competencies, and associated organizational arrangements. As these developments represent areas of broad interest to industry and society at large, it is incumbent on us as researchers to begin to formulate concrete research agendas. To facilitate this process, we propose a workshop to outline the key concerns and research directions that the application of AI methods and thinking machines to design will entail. We will specifically consider the implications of AI for design science research, organizational scholarship, and IS-oriented studies of design and innovation.

References Amabile, T. 1996. Creativity in Context, Westview Press. Hendrikx, M., Meijer, S., Van Der Velden, J., and Iosup, A. 2011. “Procedural Content Generation for Games: A Survey,” ACM Transactions on Multimedia Computing, Communications and Applications (February), pp. 1–24. Leonard-Barton, D. 1998. Wellsprings of Knowledge : Building and Sustaining the Sources of Innovation, Harvard Business School Press. Liapis, A., Yannakakis, G. N., and Togelius, J. 2014. “Computational Game Creativity,” Proceedings of the 5th International Conference on Computational Creativity, pp. 46–53. Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., and Hassabis, D. 2015. “Human-Level Control through Deep Reinforcement Learning,” Nature (518:7540), Nature Publishing Group, pp. 529–533. Seidel, S., Berente, N., Lindberg, A., Lyytinen, K., and Nickerson, J. V. 2018. “Autonomous Tools and Design: A Triple-Loop Approach to Human-Machine Learning,” Communications of the ACM (62:1), pp. 50–57. Seidel, S., Berente, N., Martinez, B., Lindberg, A., Lyytinen, K., and Nickerson, J. V. 2018. “Autonomous Tools in System Design: Reflective Practice in Ubisofts Ghost Recon Wildlands Project,” Computer (51:10), pp. 16–23.

Best, Aron Lindberg (primary contact) (aron.lindberg@stevens.edu) Stefan Seidel (stefan.seidel@uni.li) Tuure Tuunanen (tuure@tuunanen.fi) Rikard Lindgren (rikard.lindgren@ait.gu.se) Kalle Lyytinen (kalle@case.edu)

*Prof. Dr. Stefan Seidel* Professor and Chair of Information Systems & Innovation

University of Liechtenstein Institute of Information Systems Fürst-Franz-Josef-Strasse, 9490 Vaduz, Liechtenstein Phone +423 265 11 11, Direct +423 265 13 03 stefan.seidel@uni.li, www.uni.li