Exploring Defuturing to Design Artificial-Intelligence Artifacts: A Systemic-Design Approach to Tackle Litigiousness in the Brazilian Judiciary

Luciane Amaral Corrêa Münch () and Taís Schilling Ferraz ()
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Luciane Amaral Corrêa Münch: National Judicial School, Brasilia 70200-003, DF, Brazil
Taís Schilling Ferraz: National Judicial School, Brasilia 70200-003, DF, Brazil

Laws, 2024, vol. 13, issue 1, 1-13

Abstract: From the perspective of defuturing design philosophy, this article discusses the close relationship between the growing body of artificial-intelligence (AI) artifacts in the Brazilian Judiciary and the phenomenon of litigiousness therein. Litigiousness has traditionally been tackled through mechanisms that increase productivity and efficiency in case processing, a strategy that has not succeeded in reducing litigiousness, as data make evident. Analyzing data from relevant sources, this article demonstrates that AI artifacts mostly perform tasks related to clustering and mass handling of cases, following the same path dependency. Consequently, they entail risks of judges’ alienation and loss of agency, which can negatively impact citizens’ fundamental rights. Moreover, they defuture; that is, they erase other (preferable) futures. Albeit AI artifacts can play a part in tackling litigiousness, there should be a critical reflection upon futuring and defuturing. Therefore, this article recommends that SoDF—a systemic approach to design that seeks to explore design consequences, futuring and defuturing—be mandatory to any AI design process. Additionally, it proposes continuous judicial monitoring for alienation and loss of agency, as well as investments in judicial education to empower judges to effectively control and supervise AI artifacts. Finally, it suggests a further research agenda.

Keywords: defuturing; path dependency; artificial intelligence; litigiousness; design; innovation; productivity; alienation; agency; Judiciary (search for similar items in EconPapers)
JEL-codes: D78 E61 E62 F13 F42 F68 K0 K1 K2 K3 K4 (search for similar items in EconPapers)
Date: 2024
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