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Artificial Interdisciplinarity: Artificial Intelligence for Research on Complex Societal Problems

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Abstract

This paper considers the question: In what ways can artificial intelligence assist with interdisciplinary research for addressing complex societal problems and advancing the social good? Problems such as environmental protection, public health, and emerging technology governance do not fit neatly within traditional academic disciplines and therefore require an interdisciplinary approach. However, interdisciplinary research poses large cognitive challenges for human researchers that go beyond the substantial challenges of narrow disciplinary research. The challenges include epistemic divides between disciplines, the massive bodies of relevant literature, the peer review of work that integrates an eclectic mix of topics, and the transfer of interdisciplinary research insights from one problem to another. Artificial interdisciplinarity already helps with these challenges via search engines, recommendation engines, and automated content analysis. Future “strong artificial interdisciplinarity” based on human-level artificial general intelligence could excel at interdisciplinary research, but it may take a long time to develop and could pose major safety and ethical issues. Therefore, there is an important role for intermediate-term artificial interdisciplinarity systems that could make major contributions to addressing societal problems without the concerns associated with artificial general intelligence.

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Notes

  1. http://aideus.com/community/community.html

  2. https://deepmind.com/blog/learning-through-human-feedback

References

  • Altmann, J., & Sauer, F. (2017). Autonomous weapon systems and strategic stability. Survival, 59(5), 117–142.

    Google Scholar 

  • Armstrong, S., Sandberg, A., & Bostrom, N. (2012). Thinking inside the box: Controlling and using an oracle AI. Minds and Machines, 22(4), 299–324.

    Google Scholar 

  • Augenstein, I., Das, M., Riedel, S., Vikraman, L., & McCallum, A. (2017). Semeval 2017 task 10: Scienceie-Extracting keyphrases and relations from scientific publications. Proceedings of the International Workshop on Semantic Evaluation (SemEval at ACL 2017). https://arxiv.org/abs/1704.02853.

  • Baum, S. D. (2017a). On the promotion of safe and socially beneficial artificial intelligence. AI & SOCIETY, 32(4), 543–551.

    Google Scholar 

  • Baum, S.D. (2017b). A survey of artificial general intelligence projects for ethics, risk, and policy. Global Catastrophic Risk Institute working paper 17-1.

  • Baum, S. D. (2018). Reconciliation between factions focused on near-term and long-term artificial intelligence. AI & SOCIETY, 33(4), 565–572.

    Google Scholar 

  • Baum, S. D. (2020). Medium-term artificial intelligence and society. Information, 11(6), 290. https://doi.org/10.3390/info11060290.

    Article  Google Scholar 

  • Baum, S. D., Goertzel, B., & Goertzel, T. G. (2011). How long until human-level AI? Results from an expert assessment. Technological Forecasting and Social Change, 78(1), 185–195.

    Google Scholar 

  • Bernstein, J. H. (2015). Transdisciplinarity: a review of its origins, development, and current issues. Journal of Research Practice, 11(1), article R1.

    Google Scholar 

  • Boden, M. A. (2009). Computer models of creativity. AI Magazine, 30(3), 23–34.

    Google Scholar 

  • Bracken, L. J., & Oughton, E. A. (2006). ‘What do you mean?’ The importance of language in developing interdisciplinary research. Transactions of the Institute of British Geographers, 31(3), 371–382.

    Google Scholar 

  • Bromham, L., Dinnage, R., & Hua, X. (2016). Interdisciplinary research has consistently lower funding success. Nature, 534, 684–687.

    Google Scholar 

  • Cave, S., & Ó hÉigeartaigh, S. S. (2019). Bridging near- and long-term concerns about AI. Nature Machine Learning, 1(1), 5–6.

    Google Scholar 

  • Charlin, L., & Zemel, R. S. (2013). The Toronto Paper Matching System: An automated paper-reviewer assignment system. International conference on machine learning (ICML) 2013, Workshop on Peer Reviewing and Publishing Models.

  • Chase, W. G., & Simon, H. A. (1973). Perception in chess. Cognitive Psychology, 4(1), 55–81.

    Google Scholar 

  • Committee on Facilitating Interdisciplinary Research and Committee on Science, Engineering, and Public Policy. (2005). Facilitating interdisciplinary research. Washington, D.C.: National Academies Press.

    Google Scholar 

  • Crease, R. P. (2017). Physical sciences. In R. Frodeman (Ed.), The Oxford handbook of interdisciplinarity (Second ed., pp. 71–87). Oxford: Oxford University Press.

  • Frodeman, R. (Ed.). (2017). The Oxford handbook of interdisciplinarity (Second ed.). Oxford: Oxford University Press.

    Google Scholar 

  • Grace, K., Salvatier, J., Dafoe, A., Zhang, B., & Evans, O. (2018). When will AI exceed human performance? Evidence from AI experts. Journal of Artificial Intelligence Research, 62, 729–754.

    Google Scholar 

  • Hawkins, J. (2017). What intelligent machines need to learn from the neocortex. IEEE Spectrum, 2 June. https://spectrum.ieee.org/computing/software/what-intelligent-machines-need-to-learn-from-the-neocortex.

  • Hoffmann, M. H., Schmidt, J. C., & Nersessian, N. J. (2013). Philosophy of and as interdisciplinarity. Synthese, 190(11), 1857–1864.

    Google Scholar 

  • Holbrook, J. B. (2017). Peer review, interdisciplinarity, and serendipity. In R. Frodeman (Ed.), The Oxford handbook of interdisciplinarity (Second ed., pp. 485–497). Oxford: Oxford University Press.

  • Hukkinen, J. I. (2017). Peer review has its shortcomings, but AI is a risky fix. Wired, 30 January, https://www.wired.com/2017/01/peer-review-shortcomings-ai-risky-fix.

  • Jacobs, J. A. (2013). In defense of disciplines: Interdisciplinarity and specialization in the research university. Chicago: University of Chicago Press.

    Google Scholar 

  • Keestra, M. (2017). Metacognition and reflection by interdisciplinary experts: Insights from cognitive science and philosophy. Issues in Interdisciplinary Studies, 35, 121–169.

    Google Scholar 

  • Klein, J. T. (2017). Typologies of interdisciplinarity: the boundary work of definition. In R. Frodeman (Ed.), The Oxford handbook of interdisciplinarity (Second ed., pp. 21–34). Oxford: Oxford University Press.

  • Krohn, W. (2017). Interdisciplinary cases and disciplinary knowledge: epistemic challenges of interdisciplinary research. In R. Frodeman (Ed.), The Oxford handbook of interdisciplinarity (Second ed., pp. 40–52). Oxford: Oxford University Press.

    Google Scholar 

  • Lample, G., Ott, M., Conneau, A., Denoyer, L., & Ranzato, M. A. (2018). Phrase-based & neural unsupervised machine translation. https://arxiv.org/abs/1804.07755

  • Laudel, G. (2006). Conclave in the tower of Babel: how peers review interdisciplinary research proposals. Research Evaluation, 15(1), 57–68.

    Google Scholar 

  • Lawrence, R. J. (2015). Advances in transdisciplinarity: epistemologies, methodologies and processes. Futures, 65(2015), 1–9.

    Google Scholar 

  • Leeuwenberg, A., Vela, M., Dehdari, J., & van Genabith, J. (2016). A minimally supervised approach for synonym extraction with word embeddings. Prague Bulletin of Mathematical Linguistics, 105, 111–142.

    Google Scholar 

  • Legg, S., & Hutter, M. (2007). Universal intelligence: a definition of machine intelligence. Minds and Machines, 17(4), 391–444.

    Google Scholar 

  • MacLeod, M. (2018). What makes interdisciplinarity difficult? Some consequences of domain specificity in interdisciplinary practice. Synthese, 195(2), 697–720.

    Google Scholar 

  • Marcus, G. (2018). Deep learning: A critical appraisal. https://arxiv.org/abs/1801.00631

  • McCorduck, P. (2004). Machines who think: 25th (Anniversary ed.). Natick: AK Peters.

    Google Scholar 

  • McGregor, S. L. T. (2014). Introduction to special issue on transdisciplinarity. World Futures, 70(3–4), 161–163.

    Google Scholar 

  • Menken, S., & Keestra, M. (Eds.). (2016). An introduction to interdisciplinary research: theory and practice. Amsterdam: Amsterdam University Press.

    Google Scholar 

  • Minx, J. C., Callaghan, M., Lamb, W. F., Garard, J., & Edenhofer, O. (2017). Learning about climate change solutions in the IPCC and beyond. Environmental Science & Policy, 77, 252–259.

    Google Scholar 

  • Mohammed, N. (2020). Extracting word synonyms from text using neural approaches. International Arab Journal of Information Technology, 17(1), 45–51.

    Google Scholar 

  • Newell, W. H. (2001). A theory of interdisciplinary studies. Issues in Integrative Studies, 19, 1–25.

    Google Scholar 

  • Nunez-Mir, G. C., Iannone, B. V., Pijanowski, B. C., Kong, N., & Fei, S. (2016). Automated content analysis: addressing the big literature challenge in ecology and evolution. Methods in Ecology and Evolution, 7(11), 1262–1272.

    Google Scholar 

  • Nunez-Mir, G. C., Desprez, J. M., Iannone III, B. V., Clark, T. L., & Fei, S. (2017). An automated content analysis of forestry research: are socioecological challenges being addressed? Journal of Forestry, 115(1), 1–9.

    Google Scholar 

  • Pan, S. J., & Yang, Q. (2009). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.

    Google Scholar 

  • Parson, E., Re, R., Solow-Niederman, A., & Zeide, E. (2019a). Artificial intelligence in strategic context: an introduction. AI Pulse, 8 February, https://aipulse.org/artificial-intelligence-in-strategic-context-an-introduction.

  • Parson, E., Fyshe, A., Lizotte, D. (2019b). Artificial intelligence’s societal impacts, governance, and ethics: Introduction to the 2019 Summer Institute on AI and Society and its rapid outputs. AI Pulse, 26 September, https://aipulse.org/artificial-intelligences-societal-impacts-governance-and-ethics-introduction-to-the-2019-summer-institute-on-ai-and-society-and-its-rapid-outputs.

  • Pautasso, M., & Pautasso, C. (2010). Peer reviewing interdisciplinary papers. European Review, 18(2), 227–237.

    Google Scholar 

  • Perkel, J. (2017). Omnity opens multilingual semantic searches up to academia. Nature Jobs, 12 January, http://blogs.nature.com/naturejobs/2017/01/12/omnity-opens-multilingual-semantic-searches-up-to-academia.

  • Perkins, D. N., & Salomon, G. (1992). Transfer of learning. International Encyclopedia of Education (pp. 6452–6457). Oxford: Pergamon Press.

    Google Scholar 

  • Prunkl, C., & Whittlestone, J. (2020). Beyond near- and long-term: Towards a clearer account of research priorities in AI ethics and society. In Proceedings of the Third AAAI / ACM Annual Conference on AI, Ethics, and Society, New York.

  • Ranzato, M., Lample, G., Ott M. (2018). Unsupervised machine translation: a novel approach to provide fast, accurate translations for more languages. Facebook Code, 31 August, https://code.fb.com/ai-research/unsupervised-machine-translation-a-novel-approach-to-provide-fast-accurate-translations-for-more-languages.

  • Scholz, R. W., & Steiner, G. (2015). Transdisciplinarity at the crossroads. Sustainability Science, 10(4), 521–526.

    Google Scholar 

  • Shackelford, G. E., Kemp, L., Rhodes, C., Sundaram, L., ÓhÉigeartaigh, S. S., Beard, S., Belfield, H., Weitzdörfer, J., Avin, S., Sørebø, D., Jones, E. M., Hume, J. B., Price, D., Pyle, D., Hurt, D., Stone, T., Watkins, H., Collas, L., Cade, B. C., Johnson, T. F., Freitas-Groff, Z., Denkenberger, D., Levot, M., & Sutherland, W. J. (2020). Accumulating evidence using crowdsourcing and machine learning: A living bibliography about existential risk and global catastrophic risk. Futures, 116, 102508. https://doi.org/10.1016/j.futures.2019.102508.

    Article  Google Scholar 

  • Simon, H. A., & Hayes, J. R. (1976). The understanding process: problem isomorphs. Cognitive Psychology, 8(2), 165–190.

    Google Scholar 

  • Stockton, N. (2017). If AI can fix peer review in science, AI can do anything. Wired, 21 February. https://www.wired.com/2017/02/ai-can-solve-peer-review-ai-can-solve-anything.

  • Sutherland, W. J., & Wordley, C. F. (2018). A fresh approach to evidence synthesis. Nature, 558, 364–366.

    Google Scholar 

  • Tuhkala, A., Kärkkäinen, T., & Nieminen, P. (2018). Semi-automatic literature mapping of participatory design studies 2006-2016. In proceedings of Participatory Design Conference (PDC’18), DOI https://doi.org/10.1145/3210604.3210621.

  • Victor, D. (2015). Climate change: embed the social sciences in climate policy. Nature, 520(7545), 27–29.

    Google Scholar 

  • Wilson, E. O. (1998). Consilience: The unity of knowledge. New York: Knopf.

    Google Scholar 

  • Yampolskiy, R. V. (2012). Leakproofing singularity: Artificial intelligence confinement problem. Journal of Consciousness Studies, 19(1–2), 194–214.

    Google Scholar 

  • Yampolskiy, R. V. (2013). Turing test as a defining feature of AI-completeness. In X.-S. Yang (Ed.), Artificial intelligence, evolutionary computing and metaheuristics (pp. 3–17). Berlin: Springer.

    Google Scholar 

  • Zamir, A. R., Sax, A., Shen, W., Guibas, L. J., Malik, J., & Savarese, S. (2018). Taskonomy: Disentangling task transfer learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 3712-3722).

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Acknowledgments

Robert de Neufville, Roman Yampolskiy, Stuart Armstrong, Daniel Filan, Gorm Shackelford, Machiel Keestra, Mahendra Prasad, Josh Cowls, and two anonymous reviewers provided helpful comments on an earlier version of this paper. Any remaining errors are the author’s alone.

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    Seth D. Baum

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Baum, S.D. Artificial Interdisciplinarity: Artificial Intelligence for Research on Complex Societal Problems. Philos. Technol. 34 (Suppl 1), 45–63 (2021). https://doi.org/10.1007/s13347-020-00416-5

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