Abstract
Reports of errors committed in public contexts by facial recognition systems based on machine learning techniques have multiplied. Still, these systems have been increasingly used by the Brazilian public administration. Consequently, the following key problem is established: how can errors committed by facial recognition systems be prevented or mitigated when these systems are used for the elaboration and implementation of public policies? Guided by the understanding that algorithmic transparency is key to preventing and mitigating these errors, we empirically analysed whether, or not, the Brazilian General Data Protection Law (Lei Geral de Proteção de Dados Pessoais – LGPD, in the Portuguese acronym) has been used to promote this kind of transparency in situations in which facial recognition systems are employed. We circumscribed our study to the public transportation sector of 30 large Brazilian municipalities. To gather information, we sent a questionnaire to the municipal public agencies responsible for the public transportation system with questions about how the LGPD works in this public policy area. We used the Access to Information Law to do that. Upon legal analyses, we built an algorithmic transparency scale and found that, in the sector studied, the level of transparency is “Very Low” in most municipalities. This research finding indicates that the risk of lack of control over errors made by facial recognition systems is high. It suggests that the Brazilian public administration does not know how to use the systems in question ethically, and that this lack of knowledge may apply to other Artificial Intelligence systems.
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Notes
- 1.
There are different definitions for the term “Artificial Intelligence”, which were mapped by Russell and Norvig (2016) and Sweeney (2003). As we will see throughout this section, few works have investigated the use of AI and its applications by the Brazilian public sector. In this article, we cannot analyse each of them. We only mention that, in all of them, the AI systems analysed seem to adhere to the OECD (Organization for Economic Co-operation and Development) definition. For this reason, we also adopted this definition.
- 2.
- 3.
Despite its name, the purpose of the LGPD is to protect the personal data subject, and not the personal data itself.
- 4.
A controller of personal data is any “natural or legal person […] who is responsible for decisions concerning the processing of personal data” (Article 5, Item VI).
- 5.
- 6.
In Campinas, João Pessoa, and Rio Branco, the use of FR systems in public transportation was suspended at times during the Covid-19 pandemic, as the use of masks negatively interfered with the functioning of the technology.
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Acknowledgements
The authors are grateful to Professor Glauco Arbix for his invaluable comments on previous versions of this work.
Funding
The authors of this work would like to thank the C4AI-USP and the support from the São Paulo Research Foundation (FAPESP grant #2019/07665-4) and from the IBM Corporation.
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Brandão, R. et al. (2022). Artificial Intelligence, Algorithmic Transparency and Public Policies: The Case of Facial Recognition Technologies in the Public Transportation System of Large Brazilian Municipalities. In: Xavier-Junior, J.C., Rios, R.A. (eds) Intelligent Systems. BRACIS 2022. Lecture Notes in Computer Science(), vol 13653. Springer, Cham. https://doi.org/10.1007/978-3-031-21686-2_39
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