Estratégias de comunicação do Consentimento Informado e rastros de Padrões Obscuros no Instagram
Resumo
Pesquisas têm alertado sobre riscos de novas tecnologias com enorme poder computacional e de processamento se aliadas ao grande volume de dados advindos da crescente utilização de redes sociais. Um dos riscos está relacionado a privacidade e a propriedade dos dados pessoais. Este trabalho investigou a comunicabilidade do Instagram com o objetivo de identificar as estratégias de comunicação desta rede social sobre o consentimento informado. Para isso, aplicamos o Método de Inspeção Semiótica, um método com foco na avaliação da comunicabilidade de sistemas interativos. Os resultados indicam violações do princípio ético de privacidade relacionado ao consentimento e a presença de Padrões Obscuros nas estratégias de comunicação do Instagram.
Palavras-chave:
Engenharia Semiótica, Privacidade, Ética, Redes Sociais, Interação Humano-Computador, Padrões Obscuros
Referências
AEPD (2019). A guide to privacy by design.
Ahlgren, M. (2021). 40+ instagram statistics & facts. [link]. Acesso em: 28 out. 2021.
Barbosa, S. and Silva, B. (2010). Interação humano-computador. Elsevier Brasil.
Barreto, P., Salgado, L., and Viterbo, J. (2018a). Assessing the communicability of human- data interaction mechanisms in transparency enhancing tools. In 2018 Federated Conference on Computer Science and Information Systems (FedCSIS), pages 897–906.
Barreto, P., Salgado, L., and Viterbo, J. (2018b). Transparency communication strategies in human-data interaction. New York, NY, USA. Association for Computing Machinery.
Bindu, T. H. (2017). Deducing private information from social network using unified classification. Journal of Science and Technology (JST), 2(3):41–46.
BRASIL (1988). Constituição da república federativa do brasil de 1988.
Brignull, H. (2010). Dark patterns. https://www.darkpatterns.org/. Acesso em: 2 sep. 2021.
Brito do Rêgo, B., Monteiro, I. T., and Sampaio, A. L. (2017). Communicability evaluation of privacy settings on facebook for android. In International Conference on Human Aspects of Information Security, Privacy, and Trust, pages 623–639. Springer.
Cavoukian, A. (2009). Privacy by design.
Chen, J., Hsieh, G., Mahmud, J. U., and Nichols, J. (2014). Understanding individuals’ personal values from social media word use. In Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing, pages 405–414.
Coopamootoo, P. L. and Ashenden, D. (2011). A systematic evaluation of the communicability of online privacy mechanisms with respect to communication privacy management. In International Conference of Design, User Experience, and Usability, pages 384–393. Springer.
de Almeida, M. C. G. and de Castro Salgado, L. C. (2019). Investigating google dashboard’s explainability to support individual privacy decision making. In Proceedings of the 18th Brazilian Symposium on Human Factors in Computing Systems, IHC ’19, New York, NY, USA. Association for Computing Machinery.
de Rezende Xavier, S. I. (2014). Privacidade em redes sociais: uma análise da experiência dos usuários. Master’s thesis, Universidade Federal de Minas Gerais.
de Souza, C. S. (2005). The semiotic engineering of human-computer interaction. MIT press.
de Souza, C. S. and Leitão, C. F. (2009). Semiotic engineering methods for scientific research in hci. Synthesis Lectures on Human-Centered Informatics, 2(1):1–122.
de Souza, C. S., Leitão, C. F., Prates, R. O., and Da Silva, E. J. (2006). The semiotic inspection method. In Proceedings of VII Brazilian symposium on Human factors in computing systems, pages 148–157.
de Souza, C. S., Leitão, C. F., Prates, R. O., Amélia Bim, S., and da Silva, E. J. (2010). Can inspection methods generate valid new knowledge in hci? the case of semiotic inspection. International Journal of Human-Computer Studies, 68(1):22–40.
dos Santos, G. E., Barbosa, M. W., and Barbosa, G. A. (2016). Caracterização das estratégias de privacidade do instagram. In Anais do XIII Simpósio Brasileiro de Sistemas Colaborativos, pages 1275–1289. SBC.
European Union (2016). General data protection regulation. Official Journal of the European Union, page 48.
Fjeld, J., Achten, N., Hilligoss, H., Nagy, A., and Srikumar, M. (2020). Principled artificial intelligence: Mapping consensus in ethical and rights-based approaches to principles for ai. Berkman Klein Center Research Publication, (1).
Gray, C. M., Kou, Y., Battles, B., Hoggatt, J., and Toombs, A. L. (2018). The dark (patterns) side of ux design. In Proceedings of the 2018 CHI conference on human factors in computing systems, pages 1–14.
HLEGAI (2019). High-level expert group on artificial intelligence. ethics guidelines for trustworthy ai. [link].
Kemp, S. (2020). Social media users pass the 4 billions mark as global adoptionsoars.. [s. l.], 20 out. 2020. [link]. Acesso em: 10 dez. 2010.
Kemp, S. (2022). Digital 2022: Another year of bumper growth, 26 jan. 2022. [link]. Acesso em: 7 apr. 2022.
Kizza, J. M. (2003). Ethical and social issues in the information age. Springer.
Kosinski, M., Stillwell, D., and Graepel, T. (2013). Private traits and attributes are predictable from digital records of human behavior. Proceedings of the national academy of sciences, 110(15):5802–5805.
Lewis, S. (2015). Qualitative inquiry and research design: Choosing among five approaches. Health promotion practice, 16(4):473–475.
Majedi, M. and Barker, K. (2021). The privacy policy permission model: A unified view of privacy policies. Trans. Data Priv., 14(1):1–36.
Meier, Y., Schäwel, J., and Krämer, N. C. (2020). The shorter the better? effects of privacy policy length on online privacy decision-making. Media and Communication, 8(2):291–301.
Moor, J. H. (1985). What is computer ethics? Metaphilosophy, 16(4):266–275.
Ohm, P. (2009). Broken promises of privacy: Responding to the surprising failure of anonymization. UCLA l. Rev., 57:1701.
ONU (1948). Declaração universal dos direitos humanos.
PRESIDÊNCIA DA REPÚBLICA (2018). Lei nº 13.709, de 14 de agosto de 2018. lei geral de proteção de dados pessoais (lgpd).
Recuero, R. (2007). Considerações sobre a difusão de informações em redes sociais na internet. Intercom Sul.
Rocasolano, M. M. (2022). Human rights, big data and artificial intelligence: Elements of a complex algorithm. In Security and Defence: Ethical and Legal Challenges in the Face of Current Conflicts, pages 93–102. Springer.
Vianna, T. (2007). Transparência pública, opacidade privada. Editora Revan, 1st edition.
Wang, Y. and Kosinski, M. (2018). Deep neural networks are more accurate than humans at detecting sexual orientation from facial images. Journal of personality and social psychology, 114(2):246.
Ahlgren, M. (2021). 40+ instagram statistics & facts. [link]. Acesso em: 28 out. 2021.
Barbosa, S. and Silva, B. (2010). Interação humano-computador. Elsevier Brasil.
Barreto, P., Salgado, L., and Viterbo, J. (2018a). Assessing the communicability of human- data interaction mechanisms in transparency enhancing tools. In 2018 Federated Conference on Computer Science and Information Systems (FedCSIS), pages 897–906.
Barreto, P., Salgado, L., and Viterbo, J. (2018b). Transparency communication strategies in human-data interaction. New York, NY, USA. Association for Computing Machinery.
Bindu, T. H. (2017). Deducing private information from social network using unified classification. Journal of Science and Technology (JST), 2(3):41–46.
BRASIL (1988). Constituição da república federativa do brasil de 1988.
Brignull, H. (2010). Dark patterns. https://www.darkpatterns.org/. Acesso em: 2 sep. 2021.
Brito do Rêgo, B., Monteiro, I. T., and Sampaio, A. L. (2017). Communicability evaluation of privacy settings on facebook for android. In International Conference on Human Aspects of Information Security, Privacy, and Trust, pages 623–639. Springer.
Cavoukian, A. (2009). Privacy by design.
Chen, J., Hsieh, G., Mahmud, J. U., and Nichols, J. (2014). Understanding individuals’ personal values from social media word use. In Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing, pages 405–414.
Coopamootoo, P. L. and Ashenden, D. (2011). A systematic evaluation of the communicability of online privacy mechanisms with respect to communication privacy management. In International Conference of Design, User Experience, and Usability, pages 384–393. Springer.
de Almeida, M. C. G. and de Castro Salgado, L. C. (2019). Investigating google dashboard’s explainability to support individual privacy decision making. In Proceedings of the 18th Brazilian Symposium on Human Factors in Computing Systems, IHC ’19, New York, NY, USA. Association for Computing Machinery.
de Rezende Xavier, S. I. (2014). Privacidade em redes sociais: uma análise da experiência dos usuários. Master’s thesis, Universidade Federal de Minas Gerais.
de Souza, C. S. (2005). The semiotic engineering of human-computer interaction. MIT press.
de Souza, C. S. and Leitão, C. F. (2009). Semiotic engineering methods for scientific research in hci. Synthesis Lectures on Human-Centered Informatics, 2(1):1–122.
de Souza, C. S., Leitão, C. F., Prates, R. O., and Da Silva, E. J. (2006). The semiotic inspection method. In Proceedings of VII Brazilian symposium on Human factors in computing systems, pages 148–157.
de Souza, C. S., Leitão, C. F., Prates, R. O., Amélia Bim, S., and da Silva, E. J. (2010). Can inspection methods generate valid new knowledge in hci? the case of semiotic inspection. International Journal of Human-Computer Studies, 68(1):22–40.
dos Santos, G. E., Barbosa, M. W., and Barbosa, G. A. (2016). Caracterização das estratégias de privacidade do instagram. In Anais do XIII Simpósio Brasileiro de Sistemas Colaborativos, pages 1275–1289. SBC.
European Union (2016). General data protection regulation. Official Journal of the European Union, page 48.
Fjeld, J., Achten, N., Hilligoss, H., Nagy, A., and Srikumar, M. (2020). Principled artificial intelligence: Mapping consensus in ethical and rights-based approaches to principles for ai. Berkman Klein Center Research Publication, (1).
Gray, C. M., Kou, Y., Battles, B., Hoggatt, J., and Toombs, A. L. (2018). The dark (patterns) side of ux design. In Proceedings of the 2018 CHI conference on human factors in computing systems, pages 1–14.
HLEGAI (2019). High-level expert group on artificial intelligence. ethics guidelines for trustworthy ai. [link].
Kemp, S. (2020). Social media users pass the 4 billions mark as global adoptionsoars.. [s. l.], 20 out. 2020. [link]. Acesso em: 10 dez. 2010.
Kemp, S. (2022). Digital 2022: Another year of bumper growth, 26 jan. 2022. [link]. Acesso em: 7 apr. 2022.
Kizza, J. M. (2003). Ethical and social issues in the information age. Springer.
Kosinski, M., Stillwell, D., and Graepel, T. (2013). Private traits and attributes are predictable from digital records of human behavior. Proceedings of the national academy of sciences, 110(15):5802–5805.
Lewis, S. (2015). Qualitative inquiry and research design: Choosing among five approaches. Health promotion practice, 16(4):473–475.
Majedi, M. and Barker, K. (2021). The privacy policy permission model: A unified view of privacy policies. Trans. Data Priv., 14(1):1–36.
Meier, Y., Schäwel, J., and Krämer, N. C. (2020). The shorter the better? effects of privacy policy length on online privacy decision-making. Media and Communication, 8(2):291–301.
Moor, J. H. (1985). What is computer ethics? Metaphilosophy, 16(4):266–275.
Ohm, P. (2009). Broken promises of privacy: Responding to the surprising failure of anonymization. UCLA l. Rev., 57:1701.
ONU (1948). Declaração universal dos direitos humanos.
PRESIDÊNCIA DA REPÚBLICA (2018). Lei nº 13.709, de 14 de agosto de 2018. lei geral de proteção de dados pessoais (lgpd).
Recuero, R. (2007). Considerações sobre a difusão de informações em redes sociais na internet. Intercom Sul.
Rocasolano, M. M. (2022). Human rights, big data and artificial intelligence: Elements of a complex algorithm. In Security and Defence: Ethical and Legal Challenges in the Face of Current Conflicts, pages 93–102. Springer.
Vianna, T. (2007). Transparência pública, opacidade privada. Editora Revan, 1st edition.
Wang, Y. and Kosinski, M. (2018). Deep neural networks are more accurate than humans at detecting sexual orientation from facial images. Journal of personality and social psychology, 114(2):246.
Publicado
31/07/2022
Como Citar
LIMA, Patrícia Raposo Santana; SALGADO, Luciana Cardoso de Castro.
Estratégias de comunicação do Consentimento Informado e rastros de Padrões Obscuros no Instagram. In: WORKSHOP SOBRE AS IMPLICAÇÕES DA COMPUTAÇÃO NA SOCIEDADE (WICS), 3. , 2022, Niterói.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 40-54.
ISSN 2763-8707.
DOI: https://doi.org/10.5753/wics.2022.223169.
