Transparency on the use of Artificial Intelligence in the Judiciary: a governance framework
DOI:
https://doi.org/10.53798/suprema.2023.v3.n2.a231Keywords:
Artificial Intelligence, Transparency, Governance, RegulationAbstract
This article proposes, based on a bibliographic review of the transparency matrix presented by Kaminski, a transparency governance model in the Brazilian Judiciary on Artificial Intelligence systems (such as socio-technical systems), based on the segmentation of the transparency object into: transparency about: the use, operation and benefits and risks of the Artificial Intelligence (AI) system. The model specifies what the key questions and indications would be to define relevant and appropriate information content for different contexts and interlocutors, both internal participants (servants and magistrates, Information Technology (IT) servers, etc.) and external recipients (citizens, lawyers, etc.). The matrix presented, in addition to enabling democratic public control of AI projects, serves as a basis for empirical studies on the transparency of the Courts in relation to the tools used. Courts will also be able, based on the model created in this article, to develop internal transparency governance policies applicable to the AI systems they use.
Downloads
References
ANDERSON, Chris. The end of theory: the data deluge makes the scientific method obsolete. Wired, 23 June 2000. Disponível em: https://www.wired.com/2008/06/pb-theory/. Acesso em: 3 out. 2023.
ARRIETA, Alejandro Barredo et al. Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, v. 58, p. 82-115, June 2020. Disponível em: https://arxiv.org/pdf/1910.10045. Acesso em: 3 out. 2023.
BARFIELD, Woodrow (ed.). The Cambridge handbook of the law of algorithms. Cambridge: Cambridge University Press, 2021.
BURRELL, Jenna. How the machine thinks: understanding opacity in machine learning algorithms. Big Data & Society, v. 3, n. 1, Jan. 2016. Disponível em: https://doi.org/10.1177/2053951715622512. Acesso em: 3 out. 2023.
BUSUIOC, Madalina. Accountable artificial intelligence: holding algorithms to account. Public Administration Review, v. 81, n. 5, p. 825-836, Sept./Oct. 2021. Disponível em: https://onlinelibrary.wiley.com/doi/epdf/10.1111/puar.13293. Acesso em: 3 out. 2023.
COECKELBERGH, Mark. AI ethics. Cambridge, MA: MIT Press, 2020.
DIAKOPOULOS, Nicholas. Transparency. In: DUBBER, Markus D.; PASQUALE, Frank; DAS, Sunit (ed.). The Oxford handbook of ethics of AI. New York: Oxford University Press, 2020. p. 197-213.
DIGNUM, Virginia. Ethics in artificial intelligence: introduction to the special issue. Ethics and Information Technology, v. 20, n. 1, p. 1-3, 2018. Disponível em: https://link.springer.com/article/10.1007/s10676-018-9450-z. Acesso em: 3 out. 2023.
DIGNUM, Virginia. Responsible artificial intelligence: how to develop and use AI in a responsible way. Cham, Switzerland: Springer Nature, 2019. (Artificial intelligence: foundations, theory, and algorithms).
ENGSTROM, David Freeman; HO, Daniel E. Artificially intelligent government: a review and agenda. In: VOGL, Roland (ed.). Research handbook on big data law. Cheltenham, UK: Edward Elgar, 2021. p. 57-86. Disponível em: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3551549. Acesso em: 3 out. 2023.
FLORIDI, Luciano. The end of an era: from self-regulation to hard law in the digital industry. Philosophy & Technology, v. 34, n.4, p. 619-622, Dec. 2021. Disponível em: https://link.springer.com/article/10.1007/s13347-021-00493-0. Acesso em: 5 out. 2023.
FLORIDI, Luciano et al. AI4people – an ethical framework for a good AI society: opportunities, risks, principles, and recommendations. Minds and Machines, v. 28, n. 4, p. 689-707, Nov. 2018. Disponível em: https://link.springer.com/article/10.1007/s11023-018-9482-5. Acesso em: 3 out. 2023.
FLORIDI, Luciano et al. capAI: a procedure for conducting conformity assessment of AI systems in line with the EU Artificial Intelligence Act. Oxford, UK: Saïd Business School, University of Oxford, 2022. Disponível em: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4064091. Acesso em: 4 out. 2023.
GAETANI, Francisco; ALMEIDA, Virgilio. Inteligência artificial e democracia. Valor Econômico, São Paulo, 22 jul. 2021. Disponível em: https://valor.globo.com/opiniao/coluna/inteligencia-artificial-e-democracia.ghtml. Acesso em: 23 fev. 2023.
JOBIN, Anna; IENCA, Marcello; VAYENA, Effy. The global landscape of AI ethics guidelines. Nature Machine Intelligence, v. 1, p. 389-399, 2019. Disponível em: https://www.nature.com/articles/s42256-019-0088-2. Acesso em: 3 out. 2023.
JUNQUILHO, Tainá Aguiar. Inteligência artificial no direito: limites éticos. São Paulo: Juspodivm, 2022.
JUNQUILHO, Tainá Aguiar; SUGAI, Kin Modesto; VALENTE, Ana Cláudia de Souza. A inteligência artificial no TJDFT: estratégias de publicidade e princípio da transparência. In: FARRANHA, Ana Cláudia. Direito ao acesso à informação: perspectivas no âmbito da tecnologia e governança digital. Campinas: Pontes, 2022. p. 163-178.
KAMINSKI, Margot E. Understanding transparency in algorithmic accountability. In: BARFIEL, Woodrow (ed.). Forthcoming in Cambridge handbook of the law of algorithms. Cambridge, UK: Cambridge University Press, 2021. p. 121-140. Disponível em: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3622657. Acesso em: 3 out. 2023.
LEHR, David; OHM, Paul. Playing with the data: what legal scholars should learn about machine learning. UC Davis Law Review, v. 51, n. 2, p. 653-717, Dec. 2017. Disponível em: https://lawreview.law.ucdavis.edu/issues/51/2/Symposium/51-2_Lehr_Ohm.pdf. Acesso em: 3 out. 2023.
MARANHÃO, Juliano Souza de Albuquerque; COZMAN, Fabio Gagliardi; ALMADA, Marco. Concepções de explicação e do direito à explicação de decisões automatizadas. In: VAINZOF, Rony; GUTIERREZ, Andriei Guerrero (coord.). Inteligência artificial: sociedade, economia e Estado. São Paulo: Revista dos Tribunais, 2021. p. 137-154.
MITTELSTADT, Brent. Principles alone cannot guarantee ethical AI. Nature Machine Intelligence, v. 1, 501-507, Nov. 2019. Disponível em: https://www.nature.com/articles/s42256-019-0114-4. Acesso em: 5 out. 2023.
PINHANHEZ, Claudio S. Expose uncertainty, instill distrust, avoid explanations: towards ethical guidelines for AI. In: NEURIPS 2021 WORKSHOP ON HUMANCENTERED AI, 2021, online. [Proceedings…]. Disponível em: https://arxiv.org/abs/2112.01281. Acesso em: 16 abr. 2023.
SALOMÃO, Luis Felipe (coord.). Inteligência artificial: tecnologia aplicada à gestão dos conflitos no âmbito do Poder Judiciário brasileiro. 2. ed. Rio de Janeiro: FGV Conhecimento Centro de Inovação, Administração e Pesquisa do Judiciário, 2022. Disponível em: https://ciapj.fgv.br/sites/ciapj.fgv.br/files/relatorio_ia_2fase.pdf. Acesso em: 2 fev. 2023.
WISCHMEYER, Thomas. Artificial intelligence and transparency: opening the black box. In: WISCHMEYER, Thomas; RADEMACHER, Timo (ed.). Regulating artificial intelligence. Cham, Switzerland: Springer Nature, 2020. p. 75-101.
WISCHMEYER, Thomas; RADEMACHER, Timo (ed.). Regulating artificial intelligence. Cham, Switzerland: Springer Nature, 2020.
ZHONG, Jinfeng; NEGRE, Elsa. AI: to interpret or to explain? In: CONGRÈS INFORSID 2021: INFormatique des Organisations et Systèmes d’Information et de Décision, 39., 2021, Dijon, France. [Actes…]. Disponível em: https://hal.science/hal-03529203/document. Acesso em: 3 out. 2023.
BRASIL. Congresso Nacional. Senado Federal. Projeto de Lei n° 21, de 2020:Relatório final da Comissão de Juristas responsável por subsidiar a elaboração de substitutivo sobre inteligência artificial. Diário do Senado Federal, ano 77, n. 204, suplemento n. B, p. 3-915, 9 dez. 2022. Disponível em: https://legis.senado.leg.br/diarios/BuscaDiario?tipDiario=1&datDiario=09/12/2022&paginaDireta=3&indSuplemento=Sim&codSuplemento=B&desVolumeSuplemento=&desTomoSuplemento=&_gl=1*hulqbi*_ga*MjgwNzg1MDUzLjE2MjQ0ODA3Nzk.*_ga_CW3ZH25XMK*MTY5NjUzMDI1MS4yNS4xLjE2OTY1MzA3MDcuMC4wLjA. Acesso em: 2 abr. 2023.
BRASIL. Conselho Nacional de Justiça. Portaria nº 271, de 04/12/2020. Regulamenta o uso de Inteligência Artificial no âmbito do Poder Judiciário. DJe/CNJ, n. 393, p. 2-4, 14 dez. 2020. Disponível em: https://atos.cnj.jus.br/atos/detalhar/3613. Acesso em: 7 mar. 2023.
INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS. IEEE 7000-2021: IEEE model process for addressing ethical concerns during system design. New York: IEEE, 2021. Disponível em: https://standards.ieee.org/ieee/24748-7000/11098/7000/6781/. Acesso em: 4 out. 2023.
INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS. IEEE 7001-2021: IEEE standard for transparency of autonomous systems. New York: IEEE, 2021. Disponível em: https://standards.ieee.org/ieee/7001/6929/. Acesso em: 4 out. 2023.
INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS. IEEE P2863: recommended practice for organizational governance of artificial intelligence. New York: IEEE, 2020. Disponível em: https://standards.ieee.org/ieee/2863/10142/. Acesso em: 4 out. 2023.
INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS. IEEE P2894: IEEE draft guide for an architectural framework for explainable artificial intelligence. New York: IEEE, 2023. Disponível em: https://standards.ieee.org/ieee/2894/11296/. Acesso em: 4 out. 2023.
INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS. IEEE P7003: algorithmic bias considerations. New York: IEEE, 2023. Disponível em: https://standards.ieee.org/ieee/7003/11357/. Acesso em: 4 out. 2023.
INTERNATIONAL ORGANIZATION FOR STANDARDIZATION. ISO/IEC TR 24027:2021: information technology – artificial intelligence (AI) – bias in AI systems and AI aided decision making. Geneva, Switzerland: ISO, 2021. Disponível em: https://www.iso.org/standard/77607.html. Acesso em: 4 out. 2023.
INTERNATIONAL ORGANIZATION FOR STANDARDIZATION. ISO/IEC 38507:2022: information technology – governance of IT – governance implications of the use of artificial intelligence by organizations. Disponível em: https://www.iso.org/standard/56641.html. Acesso em: 4 out. 2023.
UNIÃO EUROPEIA. Comissão Europeia. The Artificial Intelligence Act. Bruxelas: EU, 2021. Disponível em: https://artificialintelligenceact.eu/. Acesso em: 2 abr. 2023
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Juliano Souza de Albuquerque Maranhão, Tainá Aguiar Junquilho, Fernando Antônio Tasso
This work is licensed under a Creative Commons Attribution 4.0 International License.
