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An AI-Enhanced Solution for Large-Scale Deliberation Mapping and Explainable Reasoning

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Abstract

This work aims to respond to the profound lack of dialogue between citizenship and policy making institutions by proposing a novel solution that enables the transition to inclusive, transparent, accountable and trustworthy deliberation practices. The proposed solution builds on cutting-edge AI tools and technologies to develop a sustainable digital platform, and bridges theories from the fields of argumentation and digital democracy. It may transform scattered islands of emerging knowledge and practices, as well as fragmented discussion threads, into an integrated and coherent dialogue, and provides mechanisms for expanding this dialogue and converting it into tangible actions. Much attention is paid to issues related to knowledge extraction, knowledge graph-based representation of large-scale deliberation, argument mining, aggregation and visualization, as well as to explanation and awareness services about the evolution and outcome of a deliberation.

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References

  • Androutsopoulou, A., Karacapilidis, N., Loukis, E., Charalabidis, Y.: Combining technocrats’ expertise with public opinion through an innovative e-participation platform.IEEE Trans. Emerg. Top. Comput. 2018 (2018). https://doi.org/10.1109/TETC.2018.2824022

  • Bodanza, G., Tohmé, F., Auday, M.: Collective argumentation: a survey of aggregation issues around argumentation frameworks. Argument Comput. 8(1), 1–34 (2017)

    Article  Google Scholar 

  • Cabrio, E., Villata, S.: Five years of argument mining: a data-driven analysis. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI-18), pp. 5427–5433. AAAI Press (2018)

    Google Scholar 

  • Checkland, P., Holwell, S.: Action research: Its nature and validity. Syst. Pract. Action Res. 11(1), 9–21 (1998)

    Article  Google Scholar 

  • Christodoulou, S., Karacapilidis, N., Tzagarakis, M.: Exploiting alternative knowledge visualizations and reasoning mechanisms to enhance collaborative decision making. In: Tweedale, J.W., Neves-Silva, R., Jain, L.C., Phillips-Wren, G., Watada, J., Howlett, R.J. (eds.) Intelligent Decision Technology Support in Practice. SIST, vol. 42, pp. 89–106. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-21209-8_6

    Chapter  Google Scholar 

  • Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)

  • Giarelis, N., Kanakaris, N., Karacapilidis, N.: On a novel representation of multiple textual documents in a single graph. In: Czarnowski, I., Howlett, R.J., Jain, L.C. (eds.) IDT 2020. SIST, vol. 193, pp. 105–115. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-5925-9_9

    Chapter  Google Scholar 

  • Hevner, A.R., March, S.T., Park, J., Ram, S.: Design science in information systems research. MIS Q. 28(1), 75–105 (2004)

    Article  Google Scholar 

  • Karacapilidis, N., Malefaki, S., Charissiadis, A.: A novel framework for augmenting the quality of explanations in recommender systems. Intell. Decis. Technol. J. 11(2), 187–197 (2017)

    Google Scholar 

  • Karacapilidis, N., Christodoulou, S., Tzagarakis, M., Tsiliki, G., Pappis, C.: Strengthening collaborative data analysis and decision making in web communities. In: Proceedings of the 23rd International World Wide Web Conference (WWW2014), Companion Volume - Workshop on Web Intelligence and Communities, Seoul, Korea, 7–11 April 2014, pp. 1005–1010 (2014)

    Google Scholar 

  • Karacapilidis N. (ed.): Mastering Data-Intensive Collaboration and Decision Making: Cutting-Edge Research and Practical Applications in the Dicode Project. Studies in Big Data Series, vol. 5, Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-319-02612-1

  • De Liddo, A., Sándor, Á., Shum, S.B.: Contested collective intelligence: rationale, technologies, and a human-machine annotation study. Comput. Support. Coop. Work 21(4–5), 417–448 (2012)

    Article  Google Scholar 

  • Lin, H., Liu, Y., Wang, W., Yue, Y., Lin, Z.: Learning entity and relation embeddings for knowledge resolution. Proc. Comput. Sci. 108, 345–354 (2017)

    Article  Google Scholar 

  • Lippi, M., Torroni, P.: Argumentation mining: state of the art and emerging trends. ACM Trans. Internet Technol. 16, 2 (2016). Article 10. https://doi.org/10.1145/2850417

  • Liu, B.S., Zhou, Q., Ding, R.X., Palomares, I., Herrera, F.: Large-scale group decision making model based on social network analysis: trust relationship-based conflict detection and elimination. Eur. J. Oper. Res. 275(2), 737–754 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  • Peters, M., et al.: Deep contextualized word representations. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics, vol. 1, pp. 2227–2237 (2018)

    Google Scholar 

  • Rapoport, R.N.: Three dilemmas in action research. Hum. Relat. 23(6), 499–513 (1970)

    Article  Google Scholar 

  • Samek, W., Montavon, G., Vedaldi, A., Hansen, L.K., Müller, K.-R. (eds.): Explainable AI: Interpreting, Explaining and Visualizing Deep Learning. Springer, Heidelberg (2019). https://doi.org/10.1007/978-3-030-28954-6

  • Tang, M., Liao, H.: From conventional group decision making to large-scale group decision making: what are the challenges and how to meet them in big data era? A state-of-the-art survey. Omega 102141 (2019). https://doi.org/10.1016/j.omega.2019.102141

  • Wang, Q., Mao, Z., Wang, B., Guo, L.: Knowledge graph embedding: a survey of approaches and applications. IEEE Trans. Knowl. Data Eng. 29(12), 2724–2743 (2017)

    Article  Google Scholar 

  • Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: Proceedings of AAAI 2014, pp. 1112–1119 (2014)

    Google Scholar 

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Correspondence to Nikos Karacapilidis .

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Karacapilidis, N., Tsakalidis, D., Domalis, G. (2023). An AI-Enhanced Solution for Large-Scale Deliberation Mapping and Explainable Reasoning. In: Papadaki, M., Rupino da Cunha, P., Themistocleous, M., Christodoulou, K. (eds) Information Systems. EMCIS 2022. Lecture Notes in Business Information Processing, vol 464. Springer, Cham. https://doi.org/10.1007/978-3-031-30694-5_23

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  • DOI: https://doi.org/10.1007/978-3-031-30694-5_23

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