Vol. 33 (2023)
Artículos de Investigación

Advantages and disadvantages of the use of Artificial Intelligence in the public policy-cycle: analysis of international cases

Eugenio Arguelles Toache
Instituto de Investigaciones Sociales de la UNAM

Published 2023-11-29

How to Cite

Arguelles Toache, E. (2023). Advantages and disadvantages of the use of Artificial Intelligence in the public policy-cycle: analysis of international cases. Acta Universitaria, 33, 1–26. https://doi.org/10.15174/au.2023.3891

Abstract

In the last decade there has been an increasing use of Artificial Intelligence (AI) in public administration; however, the scientific study of this subject is relatively incipient since the statements about its advantages and disadvantages are based on assumptions and predictions of researchers and lack sufficient empirical evidence. The objective of this work is to analyze the advantages and disadvantages of the use of AI in the public policy-cycle to contribute to solving this gap. To this end, a comparative study of eight international cases is carried out. The analysis shows that the main advantage of using AI is that it allows processing and analyzing a large amount and diversity of information immediately to automate various processes within the public policy-cycle; however, there are disadvantages such as exclusion, biases in estimates, lack of privacy, and lack of transparency.

References

  1. Agyemang, F. S. K., Memon, R., Wolf, L. J., & Fox, S. (2023). High-resolution rural poverty mapping in Pakistan with ensemble deep learning. Plos One, 18(4), e0283938. https://doi.org/10.1371/journal.pone.0283938
  2. Andersen, R. (2020). The panopticon is already here. The Atlantic. https://www.theatlantic.com/magazine/archive/2020/09/china-ai-surveillance/614197/
  3. Androutsopoulou, A., Karacapilidis, N., Loukis, E., & Charalabidis, Y. (2019). Transforming the communication between citizens and government through AI-guided chatbots. Government Information Quarterly, 36(2), 358-367. https://doi.org/10.1016/j.giq.2018.10.001
  4. Astorga, A., & Facio, L. (2009). ¿Qué son y para qué sirven las políticas públicas?. Contribuciones a las ciencias sociales, 5. 1-29.
  5. https://www.eumed.net/rev/cccss/05/aalf.htm
  6. Banco Interamericano de Desarrollo (BID). (s.f.). Distancia2. https://fairlac.iadb.org/piloto/distancia2
  7. Beall, A. (30 de mayo de 2018). In China, Alibaba’s data-hungry AI is controlling (and watching) cities. Wired. https://www.wired.co.uk/article/alibaba-city-brain-artificial-intelligence-china-kuala-lumpur
  8. Berchi, M. (22 de octubre de 2019). Prometea, inteligencia artificial para hacer Justicia. Ámbito. https://www.ambito.com/politica/justicia/prometea-inteligencia-artificial-hacer-n5061091
  9. Birckland, T. (2016). An introduction to the policy process: Theories, concepts, and models of public policy making. Routledge.
  10. Blumenstock, J., Cadamuro, G., & On, R. (2015). Predicting poverty and wealth from mobile phone metadata. Science, 350(6264), 1073-1076. https://www.science.org/doi/10.1126/science.aac4420
  11. Caprotti, F., & Liu, D. (2022). Platform urbanism and the Chinese smart city: the co-production and territorialisation of Hangzhou City Brain. GeoJournal, 87, 1559-1573. https://doi.org/10.1007/s10708-020-10320-2
  12. Carlizzi, D. N., & Quattrone, A. (2022). Artificial Intelligence and Data Governance for Precision ePolicy Cycle. En D. Marino, & M. Monaca (eds.), Artificial Intelligence and Economics: the key to the Future. Springer. https://doi.org/10.1007/978-3-031-14605-3_6
  13. Corvalán, J. G. (2018). Inteligencia artificial: retos, desafíos y oportunidades-Prometea: la primera inteligencia artificial de Latinoamérica al servicio de la Justicia. Revista de Investigações Constitucionais, 5, 295-316. https://doi.org/10.5380/rinc.v5i1.55334
  14. Criado, J. I. (2021). Inteligencia artificial (y administración pública). EUNOMÍA. Revista en Cultura de la Legalidad, (20), 348-372. https://doi.org/10.20318/eunomia.2021.6097
  15. Díaz, C. (1998). El ciclo de las políticas públicas locales. Notas para su abordaje y reconstrucción. En J. C. Venesia (comp.), Políticas públicas y desarrollo local (pp. 67-108). Instituto de Desarrollo Regional. https://blogs.ead.unlp.edu.ar/introdsocio3/files/2017/10/Cristina-Diaz-El-ciclo-de-las-politicas-publicas-locales.pdf
  16. Dunn, W. N. (2012). Public policy analysis (5a ed.). Routledge. https://accord.edu.so/course/material/public-policy-and-analysis-480/pdf_content
  17. El imparcial (22 de febrero de 2020). ¿Puede la IA señalar brotes de enfermedades más rápido que los humanos? El Imparcial. https://www.elimparcial.com/tecnologia/Puede-la-IA-senalar-brotes-de-enfermedades-mas-rapido-que-los-humanos--20200222-0080.html
  18. El telégrafo (26 de junio de 2020). ¿Cómo funciona Distancia2, la plataforma para controlar aglomeraciones? El Telégrafo. https://www.eltelegrafo.com.ec/noticias/sociedad/6/distancia2-control-aglomeraciones-prevenir-contagios
  19. Ellison, K. (27 de marzo de 2020). Vigilancia digital de enfermedades: seguimiento de una pandemia. Knowable Magazine. https://knowablemagazine.org/article/health-disease/2020/digital-pandemic-tracking
  20. Estevez, E., Linares, S., & Fillottrani, P. (2020). PROMETEA: Transformando la administración de justicia con herramientas de inteligencia artificial. Banco Interamericano de Desarrollo. https://publications.iadb.org/es/prometea-transformando-la-administracion-de-justicia-con-herramientas-de-inteligencia-artificial
  21. Florez, I. C. (2020). Inteligencia Artificial (IA) Aplicada en el Sistema Judicial en Colombia. Revista Derecho y Realidad, 18, (35), 53- 80. https://doi.org/10.19053/16923936.v18.n35.2020.9638
  22. Freifeld, C. C., Mandl, K. D., Reis, B. Y., & Brownstein, J. S. (2008). HealthMap: global infectious disease monitoring through automated classification and visualization of Internet media reports. Journal of the American Medical Informatics Association, 15(2), 150-157. https://doi.org/10.1197/jamia.M2544
  23. Gutiérrez, F. E. (2021). “Distancia2”: nueva herramienta tecnológica para guardar el distanciamiento físico en Ecuador durante pandemia. El Ciudadano. https://www.elciudadano.com/latinoamerica/distancia2-nueva-herramienta-tecnologica-para-guardar-el-distanciamiento-fisico-en-ecuador-durante-pandemia/07/01/
  24. Gutiérrez, J. R., Ramos, E. M., & Acosta, R. (2018). Inteligencia artificial y aprendizaje máquina: Aplicaciones y tendencias. En A. Román, S. Sandoval, M. E. Cabello & J. Herrera (eds.), Tecnologías Disruptivas de información (pp. 69-80). Universidad de Colima. https://www.academia.edu/38537176/Inteligencia_artificial_y_aprendizaje_m%C3%A1quina_Aplicaciones_y_tendencias
  25. Hidalgo-Sanchis, P. (5 de marzo de 2018). Using big data and machine learning to respond to the refugee crisis in Uganda. UN Office for the Coordination of Humanitarian Affairs. https://reliefweb.int/report/uganda/using-big-data-and-machine-learning-respond-refugee-crisis-uganda
  26. Höchtl, J., Parycek, P., & Schöllhammer, R. (2016). Big data in the policy cycle: Policy decision making in the digital era. Journal of Organizational Computing and Electronic Commerce, 26(1–2), 147–169. https://doi.org/10.1080/10919392.2015.1125187
  27. Horton, C. (6 de septiembre de 2018). La tecnología que hizo de Taiwán un ejemplo de democracia participativa. MIT Technology. https://www.technologyreview.es//s/10483/la-tecnologia-que-hizo-de-taiwan-un-ejemplo-de-democracia-participativa
  28. Howlett, M., McConnell, A., & Perl, A. (2017). Moving policy theory forward: Connecting multiple stream and advocacy coalition frameworks to policy cycle models of analysis. Australian Journal of Public Administration, 76(1), 65–79. https://doi.org/10.1111/1467-8500.12191
  29. Hsiao, Y. T., Lin, S. Y., Tang, A., Narayanan, D., & Sarahe, C. (2018). vTaiwan: An empirical study of open consultation process in Taiwan. SocArXiv (2018).
  30. Imran, M., Castillo, C., Lucas, J., Meier, P., & Vieweg, S. (2014). AIDR: Artificial intelligence for disaster response. Proceedings of the 23rd international conference on world wide web (pp. 159-162). https://doi.org/10.1145/2567948.2577034
  31. International Telecommunication Union [ITU] (2021). United Nations Activities on Artificial Intelligence (AI) 2021. https://www.itu.int/dms_pub/itu-s/opb/gen/S-GEN-UNACT-2021-PDF-E.pdf
  32. Jann, W., & Wegrich, K. (2007). Theories of the policy cycle. Handbook of Public Policy Analysis. Theory, Politics, and Methods, 125, 43–62.
  33. Jean, N., Burke, M., Xie, M., Davis, W. M., Lobell, D. B., & Ermon, S. (2016). Combining satellite imagery and machine learning to predict poverty. Science, 353(6301), 790-794. https://doi.org/10.1126/science.aaf7894
  34. Kellenberger, B., Vargas-Muñoz, J. E., Tuia, D., Daudt, R. C., Schindler, K., Whelan, T. T. T., Ayo, B., Ofli, F., & Imran, M. (2021). Mapping vulnerable populations with AI. Cornell University. https://arxiv.org/abs/2107.14123
  35. Keller, M., Blench, M., Tolentino, H., Freifeld, C. C., Mandl, K. D., Mawudeku, A., Eysenbach, G., & Brownstein, J. S. (2009). Use of unstructured event-based reports for global infectious disease surveillance. Emerging Infectious Diseases, 15(5), 689. http://doi.org/10.3201/eid1505.081114
  36. Khattar, A., & Quadri, S. M. K. (2020). Emerging role of artificial intelligence for disaster management based on microblogged communication. Proceedings of the International Conference on Innovative Computing & Communications (ICICC) 2020. https://dx.doi.org/10.2139/ssrn.3562973
  37. Koniakou, V. (2023). From the “rush to ethics” to the “race for governance” in Artificial Intelligence. Information Systems Frontiers, 25, 71-102. https://doi.org/10.1007/s10796-022-10300-6
  38. La Nación (27 de septiembre de 2020). COVID-19: Tecnología mide distanciamiento entre personas en las calles. La Nación. https://www.lanacion.com.py/tendencias/2020/09/27/covid-19-tecnologia-mide-distanciamiento-entre-personas-en-las-calles/
  39. Lyon, A., Nunn, M., Grossel, G., & Burgman, M. (2012). Comparison of Web‐Based biosecurity intelligence systems: BioCaster, EpiSPIDER and HealthMap. Transboundary and Emerging Diseases, 59(3), 223-232. https://doi.org/10.1111/j.1865-1682.2011.01258.x
  40. Manasi, A., Panchanadeswaran, S., Sours, E., & Lee, S. J. (2022). Mirroring the bias: gender and artificial intelligence. Gender, Technology and Development, 26(3), 295-305. https://doi.org/10.1080/09718524.2022.2128254
  41. Marinucci, L., Mazzuca, C., & Gangemi, A. (2023). Exposing implicit biases and stereotypes in human and artificial intelligence: state of the art and challenges with a focus on gender. AI & SOCIETY, 38(2), 747-761. https://doi.org/10.1007/s00146-022-01474-3
  42. Mead, G., & Barbosa, B. (2023). Contested delegation: understanding critical public responses to algorithmic decision-making in the UK and Australia. The Sociological Review, 71(3), 601-623. https://doi.org/10.1177/00380261221105380
  43. Mikalef, P., & Gupta, M. (2021). Artificial intelligence capability: conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance. Information & Management, 58(3), 103434. https://doi.org/10.1016/j.im.2021.103434
  44. Misuraca, G., Barcevicius, E., & Codagnone, C. (2020). Exploring Digital Government transformation in the EU. Understanding public sector innovation in a data-driven society. Publications Office of the European Union. https://data.europa.eu/doi/10.2760/480377
  45. Nguyen, D. T., Ofli, F., Imran, M., & Mitra, P. (2017). Damage assessment from social media imagery data during disasters. Proceedings of the 2017 IEEE/ACM international conference on advances in social networks analysis and mining 2017 (pp. 569-576). https://doi.org/10.1145/3110025.3110109
  46. Ofli, F., Imran, M., & Alam, F. (2020). Using artificial intelligence and social media for disaster response and management: an overview. AI and Robotics in Disaster Studies, 63-81. https://link.springer.com/chapter/10.1007/978-981-15-4291-6_5
  47. Pashentsev, E. (2021). The malicious use of artificial intelligence through agenda setting: challenges to political stability. Proceedings of the 3rd European Conference on the Impact of Artificial Intelligence and Robotics ECIAIR (pp. 138-144).
  48. Pencheva, I., Esteve, M., & Mikhaylov, S. J. (2018). Big data and AI—A transformational shift for government: So, what next for research? Public Policy and Administration, 35(1), 1–21. https://doi.org/10.1177/0952076718780537
  49. Riobo, A., Márquez, J., & Calatayud, A. (2020). Distancia2: inteligencia artificial para una movilidad más segura en época de COVID. Banco Interamericano de Desarrollo (BID). https://blogs.iadb.org/transporte/es/distancia2-inteligencia-artificial-para-una-movilidad-mas-segura-en-epoca-de-covid/#:~:text=Ahora%20bien%2C%20los%20estudios%20disponibles,mitiga%20el%20riesgo%20de%20contagio
  50. Rodríguez, N. S. (2018). Tendencias actuales en la evaluación de políticas públicas. Ensayos de Economía, 28(53), 15-35. https://doi.org/10.15446/ede.v28n53.75382
  51. Ronzhyn, A., & Wimmer, M. (2018). Report for Electronic Governance research and practice worldwide. European Commission. https://collections.unu.edu/eserv/UNU:7600/GOV3.0_D1.1-Baseline-Research_v.0.70.pdf
  52. Ronzhyn, A., & Wimmer, M. A. (2019). Literature review of ethical concerns in the use of disruptive technologies in government 3.0. ICDS 2019: The Thirteenth International Conference on Digital Society and eGovernments, (pp. 85-93).
  53. https://www.researchgate.net/publication/331522677_Literature_Review_of_Ethical_Concerns_in_the_Use_of_Disruptive_Technologies_in_Government_30
  54. Rosenthal, A. (18 de abril de 2019). When old technology meets new: how un global pulse is using radio and ai to leave no voice behind. United Nations Fundation. https://unfoundation.org/blog/post/when-old-technology-meets-new-how-un-global-pulse-is-using-radio-and-ai-to-leave-no-voice-behind/
  55. Rouhiainen, L. (2018). Inteligencia artificial. Alienta Editorial. https://www.planetadelibros.com/libros_contenido_extra/40/39307_Inteligencia_artificial.pdf
  56. Ruvalcaba-Gómez, E. A. (2021). Inteligencia artificial en los gobiernos locales de México: análisis de percepción de los responsables de TIC. En Centro Latinoamericano de Administración para el Desarrollo (CLAD), Inteligencia artificial y ética en la gestión pública (pp. 113-137). Centro Latinoamericano de Administración para el Desarrollo (CLAD). https://www.researchgate.net/publication/350736029_Inteligencia_Artificial_aplicada_al_gobierno_una_exploracion_internacional_de_casos
  57. Sandoval-Almazán, R. (2021). Inteligencia artificial aplicada al Gobierno: una exploración internacional de casos. En Centro Latinoamericano de Administración para el Desarrollo (CLAD), Inteligencia artificial y ética en la gestión pública (pp. 159-185).
  58. Centro Latinoamericano de Administración para el Desarrollo (CLAD). https://www.researchgate.net/publication/350736029_Inteligencia_Artificial_aplicada_al_gobierno_una_exploracion_internacional_de_casos
  59. Sandoval-Almazán, R., Nuñez, J., Ibáñez, E., Valle-Cruz, D., & Ruvalcaba, E. (2020). Manual de supervivencia para la administración pública hacia la nueva normalidad (NN). Laboratorio de innovación Pública e Inteligencia Artificial. https://u-gob.com/manual-de-supervivencia-para-la-administracion-publica-hacia-la-nueva-normalidad-i-lab/
  60. Savaget, P., Chiarini, T., & Evans, S. (2019). Empowering political participation through artificial intelligence. Science and Public Policy, 46(3), 369-380. https://doi.org/10.1093/scipol/scy064
  61. Schubach, C. (2018). vTaiwan: crowdsourcing legislation in technology and beyond. Technology and Operations Management, MBA Student Perspectives. https://d3.harvard.edu/platform-rctom/submission/vtaiwan-crowdsourcing-legislation-in-technology-and-beyond/
  62. Starke, C., & Lünich, M. (2020). Artificial intelligence for political decision-making in the European Union: Effects on citizens’ perceptions of input, throughput, and output legitimacy. Data & Policy, 2, e16. https://doi.org/10.1017/dap.2020.19
  63. Sun, T. Q., & Medaglia, R. (2019). Mapping the challenges of Artificial Intelligence in the public sector: evidence from public healthcare. Government Information Quarterly, 36(2), 368-383. https://doi.org/10.1016/j.giq.2018.09.008
  64. Tingzon, I., Orden, A., Go, K. T., Sy, S., Sekara, V., Weber, I., Fatehkia, M., García-Herranz, M., & Kim, D. (2019). Mapping poverty in the philippines using machine learning, satellite imagery, and crowd-sourced geospatial information. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, Volume XLII-4/W19.
  65. https://www.researchgate.net/publication/338131416_MAPPING_POVERTY_IN_THE_PHILIPPINES_USING_MACHINE_LEARNING_SATELLITE_IMAGERY_AND_CROWD-SOURCED_GEOSPATIAL_INFORMATION
  66. Tseng, Y. S. (2022). Rethinking gamified democracy as frictional: a comparative examination of the Decide Madrid and vTaiwan platforms. Social & Cultural Geography, 1-18. https://doi.org/10.1080/14649365.2022.2055779
  67. Tuson, M. (17 de abril de 2021). Epidemiología digital: rastreando virus por internet. El heraldo. https://www.heraldo.es/noticias/sociedad/2021/04/27/epidemiologia-digital-rastreando-virus-por-internet-1487695.html
  68. UN Global Pulse (2017). Using machine learning to analyse radio talk in Uganda. United Nations Global Pulse. https://unsdg.un.org/sites/default/files/Using-machine-learning-radio-content-uganda.pdf
  69. UN Global Pulse (4 de mayo de 2021). WHO and UN Global Pulse are building a social listening radio tool to aid the COVID-19 infodemic response. United Nations Global Pulse. https://www.unglobalpulse.org/2021/05/who-and-un-global-pulse-are-building-a-social-listening-radio-tool-to-aid-the-covid-19-infodemic-response/
  70. Valle-Cruz, D., Criado, J. I., Sandoval-Almazán, R., & Ruvalcaba-Gomez, E. A. (2020). Assessing the public policy-cycle framework in the age of artificial intelligence: from agenda-setting to policy evaluation. Government Information Quarterly, 37(4), 101509. https://doi.org/10.1016/j.giq.2020.101509
  71. Vélez, M. I., Gómez, C., & Osorio, M. A. (30 de junio de 2022). Conceptos fundamentales y uso responsable de la inteligencia artificial en el sector público. Informe 2. CAF. https://scioteca.caf.com/handle/123456789/1921
  72. Wang, P. (2019). On defining artificial intelligence. Journal of Artificial General Intelligence, 10(2), 1-37. https://doi.org/10.2478/jagi-2019-0002
  73. Wirtz, B. W., Weyerer, J. C., & Geyer, C. (2019). Artificial intelligence and the public sector—applications and challenges. International Journal of Public Administration, 42(7), 596-615. https://doi.org/10.1080/01900692.2018.1498103
  74. Xie, M., Jean, N., Burke, M., Lobell, D., & Ermon, S. (2016). Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping. Proceedings of the AAAI Conference on Artificial Intelligence, 30(1). https://doi.org/10.1609/aaai.v30i1.9906
  75. Yigitcanlar, T., Corchado, J. M., Mehmood, R., Li, R. Y. M., Mossberger, K., & Desouza, K. (2021). Responsible urban innovation with local government artificial intelligence (AI): a conceptual framework and research agenda. Journal of Open Innovation: Technology, Market, and Complexity, 7(1), 71. https://doi.org/10.3390/joitmc7010071
  76. Zhang, J., Hua, X. S., Huang, J., Shen, X., Chen, J., Zhou, Q., Fu, Z., & Zhao, Y. (2019). City brain: practice of large‐scale artificial intelligence in the real world. IET Smart Cities, 1(1), 28-37. https://doi.org/10.1049/iet-smc.2019.0034
  77. Zhang, W., Zuo, N., He, W., Li, S., & Yu, L. (2021). Factors influencing the use of artificial intelligence in government: Evidence from China. Technology in Society, 66, 101675. https://doi.org/10.1016/j.techsoc.2021.101675