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Something New Versus Tried and True: Ensuring ‘Innovative’ AI is ‘Good’ AI

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Diversity, Divergence, Dialogue (iConference 2021)

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Abstract

What does it mean for AI to be innovative, and does new always mean better, particularly in terms of the ethical and societal implications of AI? This interview-driven study of 26 stakeholders of AI in the fields of technology research, law, and policy elucidates key tensions in producing innovative AI, as they are understood across sectors. As these stakeholders articulate a discourse on innovation, there is revealed a complex relationship between how innovative AI is conceived, both in terms of what is considered innovation, and how that innovation is seen to be restricted or supported through policy and regulatory action. Ultimately, this discourse operates on similar terms across these stakeholder groups, and presents a knotted, interlinked view of regulatory, design, and policy concerns across the ecology of AI, from its data to its use.

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References

  1. Berghel, H.: Malice domestic: the Cambridge analytica dystopia. Computer 51(5), 84–89 (2018). https://doi.org/10.1109/MC.2018.2381135

    Article  Google Scholar 

  2. Bürcher, S., Habersetzer, A.: Entrepreneurship in peripheral regions: a relational perspective. In: Mack, E.A., Qian, H. (eds.) Geographies of Entrepreneurship, pp. 161–182. Routledge, Taylor & Francis Group, London (2016)

    Google Scholar 

  3. Clarke, V., Braun, V., Hayfield, N.: Thematic analysis. In: Smith, J.A. (ed.) Qualitative Psychology: A Practical Guide to Research Methods, pp. 222–248. Sage Publishing, Los Angeles (2015)

    Google Scholar 

  4. Cirucci, A.M., Vacker, B. (eds.): Black Mirror and Critical Media Theory. Lexington Books, Lanham (2018)

    Google Scholar 

  5. Crivits, M., de Krom, M.P., Dessein, J., Block, T.: Why innovation is not always good: innovation discourses and political accountability. Outlook Agric. 43(3), 147–155 (2014). https://doi.org/10.5367/oa.2014.0174

    Article  Google Scholar 

  6. Dicken, P., Malmberg, A.: Firms in territories: a relational perspective. Econ. Geogr. 77(4), 345–363 (2001). https://doi.org/10.2307/3594105

    Article  Google Scholar 

  7. Elish, M.C., Boyd, D.: Situating methods in the magic of big data and AI. Commun. Monogr. 85(1), 57–80 (2018). https://doi.org/10.1080/03637751.2017.1375130

    Article  Google Scholar 

  8. Faulconbridge, J.R.: Relational geographies of knowledge and innovation. In: Bathelt, H., Cohendet, P., Henn, S., Simon, L. (eds.) The Elgar Companion to Innovation and Knowledge Creation, pp. 671–684. Edward Elgar Publishing, Northampton (2017)

    Chapter  Google Scholar 

  9. Flanagan, J.C.: The critical incident technique. Psychol. Bull. 51(4), 327–358 (1954). https://psycnet.apa.org/doi/10.1037/h0061470

  10. Fleischmann, K.R., Wallace, W.A.: Value conflicts in computational modeling. Computer 43(7), 57–63 (2010). https://doi.ieeecomputersociety.org/10.1109/MC.2010.120

  11. Floridi, L., et al.: AI4People—an ethical framework for a good AI society: opportunities, risks, principles, and recommendations. Mind. Mach. 28(4), 689–707 (2018). https://doi.org/10.1007/s11023-018-9482-5

    Article  Google Scholar 

  12. Goddard, M.: The EU general data protection regulation (GDPR): european regulation that has a global impact. Int. J. Market Res. 59(6), 703–705 (2017). https://doi.org/10.2501/IJMR-2017-050

    Article  Google Scholar 

  13. Godin, B.: The linear model of innovation: the historical construction of an analytical framework. Sci. Technol. Hum. Values 31(6), 639–667 (2006). https://doi.org/10.1177/0162243906291865

  14. Jackson, S.J., Gillespie, T., Payette, S.: The policy knot: re-integrating policy, practice and design in CSCW studies of social computing. In: Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work & Social Computing, pp. 588–602 (2014). https://doi.org/10.1145/2531602.2531674

  15. Karppi, T., Nieborg, D.B.: Facebook confessions: corporate abdication and Silicon Valley dystopianism. New Media & Society (in press). https://doi.org/10.1177/1461444820933549

  16. Knickel, K., Brunori, G., Rand, S., Proost, J.: Towards a better conceptual framework for innovation processes in agriculture and rural development: from linear models to systemic approaches. J. Agric. Educ. Extension 15(2), 131–146 (2009). https://doi.org/10.1080/13892240902909064

    Article  Google Scholar 

  17. Latour, B.: The Pasteurization of France (Alan Sheridan and John Law, trans.). Harvard University Press, Cambridge (1988)

    Google Scholar 

  18. Latour, B.: Where are the missing masses? The sociology of a few mundane artifacts. In: Bijker, W., Law, J. (eds.) Shaping Technology/Building Society, pp. 225–258. MIT Press, Cambridge (1992)

    Google Scholar 

  19. Martin, N., Matt, C., Niebel, C., Blind, K.: How data protection regulation affects startup innovation. Inf. Syst. Front. 21(6), 1307–1324 (2019). https://doi.org/10.1007/s10796-019-09974-2

    Article  Google Scholar 

  20. Minssen, T., Rajam, N., Bogers, M.: Clinical trial data transparency and GDPR compliance: implications for data sharing and open innovation. Sci. Public Policy scaa014 (in press). https://doi.org/10.2139/ssrn.3413035

  21. Mittelstadt, B.: Principles alone cannot guarantee ethical AI. Nat. Mach. Intell. 1, 501–507 (2019). https://doi.org/10.1038/s42256-019-0114-4

    Article  Google Scholar 

  22. Monroe, J.G., Woodhouse, E.J.: Averting Catastrophe: Strategies for Regulating Risky Technology. University of California Press, Berkeley (1988)

    Google Scholar 

  23. Mosco, V.: The Digital Sublime: Myth, Power, and Cyberspace. MIT Press, Cambridge (2005)

    Google Scholar 

  24. Munier, L., Kemball-Cook, A.: Blockchain and the general data protection regulation: reconciling protection and innovation. J. Securities Oper. Custody 11(2), 145–157 (2019)

    Google Scholar 

  25. Nahuis, R., Van Lente, H.: Where are the politics? Perspectives on democracy and technology. Sci. Technol. Hum. Values 33(5), 559–581 (2008). https://doi.org/10.1177/0162243907306700

  26. Nissenbaum, H.: How computer systems embody values. Computer 34(3), 120-119 (2001). https://doi.org/10.1109/2.910905

  27. Noble, D.F.: America by design: science, technology, and the rise of corporate capitalism, No. 588, Oxford University Press USA, New York (1979)

    Google Scholar 

  28. Nye, D.E.: American Technological Sublime. MIT Press, Cambridge (1996)

    Google Scholar 

  29. Phillimore, J.: Beyond the linear view of innovation in science park evaluation: an analysis of Western Australian Technology Park. Technovation 19(11), 673–680 (1999). https://doi.org/10.1016/S0166-4972(99)00062-0

    Article  Google Scholar 

  30. Shilton, K.: Values levers: building ethics into design. Sci. Technol. Hum. Values 38(3), 374–397 (2013). https://doi.org/10.1177/0162243912436985

  31. Slota, S.C., et al.: Good systems, bad data? Interpretations of AI hype and failures. In: Proceedings of the 83rd Annual Meeting of the Association for Information Science and Technology, vol. 57, no. e275, pp. 1–11 (2020). https://doi.org/10.1002/pra2.275

  32. Tufekci, Z.: How social media took us from Tahrir Square to Donald Trump. MIT Technology Review, 14 August 2018. https://www.technologyreview.com/2018/08/14/240325/how-social-media-took-us-from-tahrir-square-to-donald-trump/. Accessed 25 Sept 2020

  33. Winner, L.: Do artifacts have politics? Daedalus 109(1), 121–136 (1980). https://www.jstor.org/stable/20024652

  34. Winner, L.: The Whale and the Reactor: A Search for Limits in an Age of High Technology. University of Chicago Press, Chicago (1988)

    Book  Google Scholar 

  35. Winner, L.: Upon opening the black box and finding it empty: social constructivism and the philosophy of technology. Sci. Technol. Hum. Values 18(3), 362–378 (1993). https://doi.org/10.1177/016224399301800306

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Acknowledgements

This research was funded by Cisco Systems under RFP-16-02, Legal Implications for IoT, Machine Learning, and Artificial Intelligence. We also thank our research participants: Blake Anderson, Ruzena Bajcsy, Hal Daume III, Stephen Elkins, Enzo Fenoglio, Iria Giuffrida, Dean Harvey, James Hodson, Wei San Hui, Amir Husain, Jeff Kirk, Frederic Lederer, Ted Lehr, Terrell McSweeny, Matt Scherer, Peter Stone, Nicolas Vermeys, Christopher Yoo, and eight anonymous participants.

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Authors and Affiliations

  1. School of Information, University of Texas at Austin, Austin, TX, 78705, USA

    Stephen C. Slota, Kenneth R. Fleischmann, Nitin Verma & Lan Li

  2. LBJ School of Public Affairs, University of Texas at Austin, Austin, TX, 78705, USA

    Sherri Greenberg & Brenna Cummings

  3. Cisco Systems, Inc., Herndon, VA, USA

    Chris Shenefiel

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  1. Stephen C. Slota
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Correspondence to Stephen C. Slota .

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Editors and Affiliations

  1. Humboldt University of Berlin, Berlin, Germany

    Katharina Toeppe

  2. Renmin University of China, Beijing, China

    Hui Yan

  3. The University of Hong Kong, Hong Kong, Hong Kong

    Samuel Kai Wah Chu

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Slota, S.C. et al. (2021). Something New Versus Tried and True: Ensuring ‘Innovative’ AI is ‘Good’ AI. In: Toeppe, K., Yan, H., Chu, S.K.W. (eds) Diversity, Divergence, Dialogue. iConference 2021. Lecture Notes in Computer Science(), vol 12645. Springer, Cham. https://doi.org/10.1007/978-3-030-71292-1_3

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  • DOI: https://doi.org/10.1007/978-3-030-71292-1_3

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-71292-1

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