Desafios para a computação na implementação e implantação de solução baseada em IA em governo: uma análise da literatura
Resumo
A Inteligência Artificial (IA) tem sido adotada por governos em todo o mundo como ferramenta para o aumento de eficiência, redução de custos e melhoria na oferta de serviços públicos digitais. Embora a adoção da IA tenha aumentando significamente nos últimos anos, poucos estudos investigaram o uso da IA em governos, de modo a lançar luz sobre os desafios enfrentados ao se tentar adotar a IA em seus processos e na prestação de serviços públicos. O presente estudo apresenta uma análise da literatura baseada em um protocolo de Revisão Sistemática da Literatura para identificar quais os desafios computacionais enfrentados ao se adotar IA em governo. Alguns desafios foram elencados ao final deste trabalho, que inclui a necessidade de equipes com maior especialização de IA e outros aspectos de governança de dados e de tecnologias de informação e comunicação.
Palavras-chave:
governo digital, inteligência artificial, aprendizado de máquina
Referências
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Altaweel, M., Bone, C., and Abrams, J. (2019). Documents as data: A content analysis and topic modeling approach for analyzing responses to ecological disturbances. Ecological Informatics, 51:82–95.
Balta, D., Kuhn, P., Sellami, M., Kulus, D., Lieven, C., and Krcmar, H. (2019). How to streamline ai application in government? a case study on citizen participation in germany. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11685 LNCS:233–247.
Barnes, J. (2015). Azure machine learning. Microsoft Azure Essentials. 1st ed, Microsoft.
Campion, A., Gasco-Hernandez, M., Mikhaylov, S. J., and Esteve, M. (2020). Overcoming the challenges of collaboratively adopting artificial intelligence in the public sector. Social Science Computer Review, 0(0):0894439320979953.
Dwivedi, Y., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V., Ilavarasan, P., Janssen, M., Jones, P., Kar, A., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., Medaglia, R., Le Meunier-FitzHugh, K., Le Meunier-FitzHugh, L., Misra, S., Mogaji, E., Sharma, S., Singh, J., Raghavan, V., Raman, R., Rana, N., Samothrakis, S., Spencer, J., Tamilmani, K., Tubadji, A., Walton, P., and Williams, M. (2019). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57.
European Commission (2019). A definition of AI: Main capabilities and scientific disciplines. Technical report, High-Level Expert Group on Artificial Intelligence.
Gusenbauer, M. and Haddaway, N. (2020). Which academic search systems are suitable for systematic reviews or meta-analyses? evaluating retrieval qualities of google scholar, pubmed and 26 other resources [open access]. Research Synthesis Methods, 11:181–217.
Hagen, L., Harrison, T., Uzuner, O., Fake, T., LaManna, D., and Kotfila, C. (2015). Introducing textual analysis tools for policy informatics: A case study of e-petitions. ACM International Conference Proceeding Series, 27-30-May-2015:10–19.
Kamal, K., Kumar, M., Varyani, B., and Bhatia, K. (2015). Efficient use of voice as a channel for delivering public services. ICEIS 2015 17th International Conference on Enterprise Information Systems, Proceedings, 2:626–631.
Kang, J., Kuznetsova, P., Luca, M., and Choi, Y. (2013). Where not to eat? improving public policy by predicting hygiene inspections using online reviews. EMNLP 2013 - 2013 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference, pages 1443–1448.
Kitchenham, B. and Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering. Technical report, Technical report, EBSE Technical Report EBSE-2007-01.
Kuziemski, M. and Misuraca, G. (2020). AI governance in the public sector: Three tales from the frontiers of automated decision-making in democratic settings. Telecommunications Policy, 44(6):101976. Artificial intelligence, economy and society.
Lubis, F. and Albarda (2018). Data partition and hidden neuron value formulation combination in neural network prediction model: Case study: Non-tax revenue prediction for indonesian government unit. 2018 International Conference on Information and Communications Technology, ICOIACT 2018, 2018-January:879–884.
M. Vogl, T. (2020). Artificial intelligence and organizational memory in government: The experience of record duplication in the child welfare sector in canada. In The 21st Annual International Conference on Digital Government Research, dg.o ’20, page 223–231, New York, NY, USA. Association for Computing Machinery.
Mukherjee, S., Becker, N., Weeks, W., and Ferres, J. (2020). Using internet search trends to forecast short term drug overdose deaths: A case study on connecticut. Proceedings 19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020, pages 1332–1339.
Paramartha, I., Ardiyanto, I., and Hidayat, R. (2021). Developing machine learning framework to classify harmonized system code. case study: Indonesian customs. 3rd 2021 East Indonesia Conference on Computer and Information Technology, EIConCIT 2021, pages 254–259.
Sun, T. and Medaglia, R. (2019). Mapping the challenges of artificial intelligence in the public sector: Evidence from public healthcare. Government Information Quarterly, 36(2):368–383.
UNESCAP (2018). Frontier technologies for susteinable development in Asia and Pacific. Technical report, UNESCAP.
Zuiderwijk, A., Chen, Y.-C., and Salem, F. (2021). Implications of the use of artificial intelligence in public governance: A systematic literature review and a research agenda. Government Information Quarterly, 38(3):101577.
Altaweel, M., Bone, C., and Abrams, J. (2019). Documents as data: A content analysis and topic modeling approach for analyzing responses to ecological disturbances. Ecological Informatics, 51:82–95.
Balta, D., Kuhn, P., Sellami, M., Kulus, D., Lieven, C., and Krcmar, H. (2019). How to streamline ai application in government? a case study on citizen participation in germany. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11685 LNCS:233–247.
Barnes, J. (2015). Azure machine learning. Microsoft Azure Essentials. 1st ed, Microsoft.
Campion, A., Gasco-Hernandez, M., Mikhaylov, S. J., and Esteve, M. (2020). Overcoming the challenges of collaboratively adopting artificial intelligence in the public sector. Social Science Computer Review, 0(0):0894439320979953.
Dwivedi, Y., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V., Ilavarasan, P., Janssen, M., Jones, P., Kar, A., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., Medaglia, R., Le Meunier-FitzHugh, K., Le Meunier-FitzHugh, L., Misra, S., Mogaji, E., Sharma, S., Singh, J., Raghavan, V., Raman, R., Rana, N., Samothrakis, S., Spencer, J., Tamilmani, K., Tubadji, A., Walton, P., and Williams, M. (2019). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57.
European Commission (2019). A definition of AI: Main capabilities and scientific disciplines. Technical report, High-Level Expert Group on Artificial Intelligence.
Gusenbauer, M. and Haddaway, N. (2020). Which academic search systems are suitable for systematic reviews or meta-analyses? evaluating retrieval qualities of google scholar, pubmed and 26 other resources [open access]. Research Synthesis Methods, 11:181–217.
Hagen, L., Harrison, T., Uzuner, O., Fake, T., LaManna, D., and Kotfila, C. (2015). Introducing textual analysis tools for policy informatics: A case study of e-petitions. ACM International Conference Proceeding Series, 27-30-May-2015:10–19.
Kamal, K., Kumar, M., Varyani, B., and Bhatia, K. (2015). Efficient use of voice as a channel for delivering public services. ICEIS 2015 17th International Conference on Enterprise Information Systems, Proceedings, 2:626–631.
Kang, J., Kuznetsova, P., Luca, M., and Choi, Y. (2013). Where not to eat? improving public policy by predicting hygiene inspections using online reviews. EMNLP 2013 - 2013 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference, pages 1443–1448.
Kitchenham, B. and Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering. Technical report, Technical report, EBSE Technical Report EBSE-2007-01.
Kuziemski, M. and Misuraca, G. (2020). AI governance in the public sector: Three tales from the frontiers of automated decision-making in democratic settings. Telecommunications Policy, 44(6):101976. Artificial intelligence, economy and society.
Lubis, F. and Albarda (2018). Data partition and hidden neuron value formulation combination in neural network prediction model: Case study: Non-tax revenue prediction for indonesian government unit. 2018 International Conference on Information and Communications Technology, ICOIACT 2018, 2018-January:879–884.
M. Vogl, T. (2020). Artificial intelligence and organizational memory in government: The experience of record duplication in the child welfare sector in canada. In The 21st Annual International Conference on Digital Government Research, dg.o ’20, page 223–231, New York, NY, USA. Association for Computing Machinery.
Mukherjee, S., Becker, N., Weeks, W., and Ferres, J. (2020). Using internet search trends to forecast short term drug overdose deaths: A case study on connecticut. Proceedings 19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020, pages 1332–1339.
Paramartha, I., Ardiyanto, I., and Hidayat, R. (2021). Developing machine learning framework to classify harmonized system code. case study: Indonesian customs. 3rd 2021 East Indonesia Conference on Computer and Information Technology, EIConCIT 2021, pages 254–259.
Sun, T. and Medaglia, R. (2019). Mapping the challenges of artificial intelligence in the public sector: Evidence from public healthcare. Government Information Quarterly, 36(2):368–383.
UNESCAP (2018). Frontier technologies for susteinable development in Asia and Pacific. Technical report, UNESCAP.
Zuiderwijk, A., Chen, Y.-C., and Salem, F. (2021). Implications of the use of artificial intelligence in public governance: A systematic literature review and a research agenda. Government Information Quarterly, 38(3):101577.
Publicado
06/08/2023
Como Citar
PASCO, Carlos David R.; VITERBO, José; BERNARDINI, Flavia.
Desafios para a computação na implementação e implantação de solução baseada em IA em governo: uma análise da literatura. In: WORKSHOP DE COMPUTAÇÃO APLICADA EM GOVERNO ELETRÔNICO (WCGE), 11. , 2023, João Pessoa/PB.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 49-59.
ISSN 2763-8723.
DOI: https://doi.org/10.5753/wcge.2023.229869.
