Transparency through cooperation
How responsive regulation may help promote intelligible machine learning systems
Palabras clave:
regulacao responsiva, inteligencia artificial, vies discriminatorio, transparencia, explicabilidadeResumen
[Purpose] To analyze the applicability of the theory of responsive regulation to promote the intelligibility of machine learning systems under the focus of the Brazilian General Data Protection Law (LGPD).
[Methodology/approach/design] This article has the theory of responsive regulation as a theoretical framework and will initially be based on a comparative analysis of the LGPD and the GDPR to identify how this theory can assist Brazilian regulators, more specifically the National Data Protection Authority, to address intelligibility of artificial intelligence systems.
[Findings] From a comparative analysis between how LGPD and GDPR deal with the issue of automated decision systems (including machine-learning) explainability, this article identified that the rationale for cooperation between regulator and regulated, a network governance system and the existence of a regulatory pyramid allows for the application of the theory of responsive regulation to promote the intelligibility of these systems.
[Practical implications] AI systems have often been accused of discriminatory bias, something which may increase the racial and gender gaps in Brazil. Ensuring that the technology is understandable for humans to better identify how to address these shortcomings is paramount to promoting the use of fairer systems. This study intends, by identifying the most appropriate regulatory strategies to deal with algorithmic opacity, to assist regulators in addressing the discrimination promoted by these systems.
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