Understanding the perceived importance of disclosing ethical concerns underlying data visualizations
Resumo
Introduction: Data visualizations are increasingly being used as a means of conveying data. However, ethical issues of data visualizations are still an understudied topic. Objective: Our objective is to understand how ethical aspects relate to data visualization and how visualization readers perceived the importance of disclosing ethical concerns underlying data visualizations. Methodology: We reviewed the literature to understand how ethical aspects relate to data visualization and then evaluated, with visualization readers, what aspects they deem more important in two different scenarios. Results: The results show that the order of priority varies between scenarios and between participants’ profiles, but understanding the target audience’s needs, the visualization objective, and how the data was collected features among the most important aspects in every case. The items perceived as most important were not aligned with those found more frequently in the literature, which highlights the importance of evaluating what has been found in the literature with real participants.
Palavras-chave:
data visualization, ethics, visualization metadata
Referências
Barbosa, S. D. J., Barbosa, G. D. J., Souza, C. S. D., e Leitão, C. F. (2021). A Semiotics-based epistemic tool to reason about ethical issues in digital technology design and development. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, pages 363–374, Virtual Event Canada. ACM.
Borkin, M. A., Bylinskii, Z., Kim, N. W., Bainbridge, C. M., Yeh, C. S., Borkin, D., Pfster, H., e Oliva, A. (2016). Beyond Memorability: Visualization Recognition and Recall. IEEE Transactions on Visualization and Computer Graphics, 22(1):519–528.
Burns, A., Lee, C., On, T., Xiong, C., Peck, E., e Mahyar, N. (2024). From Invisible to Visible: Impacts of Metadata in Communicative Data Visualization. IEEE Transactions on Visualization and Computer Graphics, 30(7):3427–3443.
Burns, A., On, T., Lee, C., Shapiro, R., Xiong, C., e Mahyar, N. (2021). Making the Invisible Visible: Risks and Benefts of Disclosing Metadata in Visualization. In 2021 IEEE Workshop on Visualization for Social Good (VIS4Good), pages 11–15, New Orleans, LA, USA. IEEE.
Cabrera, A. A., Epperson, W., Hohman, F., Kahng, M., Morgenstern, J., e Chau, D. H. (2019). FAIRVIS: Visual Analytics for Discovering Intersectional Bias in Machine Learning. In 2019 IEEE Conference on Visual Analytics Science and Technology (VAST), pages 46–56, Vancouver, BC, Canada. IEEE.
Cabric, F., Bjarnadóttir, M. V., Ling, M., Rafnsdóttir, G. L., e Isenberg, P. (2024). Eleven years of gender data visualization: A step towards more inclusive gender representation. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, 30(1):10.
Cairo, A. (2019). How Charts Lie: Getting Smarter about Visual Information. W. W. Norton & Company, New York, first edition edition.
Carvalho, L. P., Suzano, J. A., Santoro, F. M., e Oliveira, J. (2022). A meta-scientific broad panorama of ethical aspects in the Brazilian IHC. Journal on Interactive Systems, 13(1):105–126.
Cavanagh, T. (2008). Diverse designing: Sorting out function and intention in artifacts. In Philosophy and Design: From Engineering to Architecture, pages 301–315. Springer Netherlands, Dordrecht.
Chen, M., Floridi, L., e Borgo, R. (2014). What Is Visualization Really For? In Floridi, L. e Illari, P., editors, The Philosophy of Information Quality, volume 358, pages 75–93. Springer International Publishing, Cham.
Chmielinski, K., Newman, S., Taylor, M., Joseph, J., Thomas, K., Yurkofsky, J., e Qiu, Y. C. (2020). The dataset nutrition label (2nd gen): Leveraging context to mitigate harms in artificial intelligence. In NeurIPS Workshop on Dataset Curation and Security.
Coeckelbergh, M. (2020). Artificial Intelligence, Responsibility Attribution, and a Relational Justification of Explainability. Science and Engineering Ethics, 26(4):2051–2068.
Coleti, T. A., Divino, S. B. S., Salgado, A. D. L., Zacarias, R. O., Saraiva, J. D. A. G., Gonçalves, D. A., Morandini, M., e Santos, R. P. D. (2024). GranDIHC-BR 2025-2035 - GC5 - Human-Data Interaction Data Literacy and Usable Privacy. In Proceedings of the XXIII Brazilian Symposium on Human Factors in Computing Systems, pages 1–24, Brasília DF Brazil. ACM.
Correll, M. (2019). Ethical Dimensions of Visualization Research. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, pages 1–13.
Correll, M. e Heer, J. (2017). Black Hat Visualization. In Workshop on Dealing with Cognitive Biases in Visualisations (DECISIVe), IEEE VIS, 2017.
D’Ignazio, C. e Klein, L. F. (2016). Feminist Data Visualization. In Workshop on Visualization for the Digital Humanities at IEEE VIS Conference 2016.
D’Ignazio, C. e Klein, L. F. (2020). Data Feminism. The MIT Press.
Dörk, M., Feng, P., Collins, C., e Carpendale, S. (2013). Critical InfoVis: Exploring the politics of visualization. In CHI ’13 Extended Abstracts on Human Factors in Computing Systems, pages 2189–2198, Paris France. ACM.
Fischer, M. T., Hirsbrunner, S. D., Jentner, W., Miller, M., Keim, D. A., e Helm, P. (2022). Promoting Ethical Awareness in Communication Analysis: Investigating Potentials and Limits of Visual Analytics for Intelligence Applications. In 2022 ACM Conference on Fairness, Accountability, and Transparency, pages 877–889, Seoul Republic of Korea. ACM.
Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Iii, H. D., e Crawford, K. (2021). Datasheets for datasets. Communications of the ACM, 64(12):86–92.
Hepworth, K. J. (2020). Make Me Care: Ethical Visualization for Impact in the Sciences and Data Sciences. In Marcus, A. e Rosenzweig, E., editors, Design, User Experience, and Usability. Interaction Design, volume 12200, pages 385–404. Springer International Publishing, Cham.
Hill, R. L., Kennedy, H., e Gerrard, Y. (2016). Visualizing Junk: Big Data Visualizations and the Need for Feminist Data Studies. Journal of Communication Inquiry, 40(4):331–350.
Holland, S., Hosny, A., Newman, S., Joseph, J., e Chmielinski, K. (2018). The dataset nutrition label: A framework to drive higher data quality standards.
Huey, H., Oey, L. A., Lloyd, H. S., e Fan, J. E. (2023). How do communicative goals guide which data visualizations people think are effective? Proceedings of the Annual Meeting of the Cognitive Science Society, 45(45).
Hullman, J. e Diakopoulos, N. (2011). Visualization Rhetoric: Framing Effects in Narrative Visualization. IEEE Transactions on Visualization and Computer Graphics, 17(12):2231–2240.
Jensen, C. e Potts, C. (2004). Privacy policies as decision-making tools: An evaluation of online privacy notices. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Chi ’04, pages 471–478, New York, NY, USA. Association for Computing Machinery.
Joshi, A., Kale, S., Chandel, S., e Pal, D. (2015). Likert Scale: Explored and Explained. British Journal of Applied Science & Technology, 7(4):396–403.
Kelley, P. G., Bresee, J., Cranor, L. F., e Reeder, R. W. (2009). A "nutrition label" for privacy. In Proceedings of the 5th Symposium on Usable Privacy and Security, pages 1–12, Mountain View California USA. ACM.
Kelley, P. G., Cranor, L. F., e Sadeh, N. (2013). Privacy as part of the app decision-making process. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pages 3393–3402, Paris France. ACM.
Kitchenham, B., Pearl Brereton, O., Budgen, D., Turner, M., Bailey, J., e Linkman, S. (2009). Systematic literature reviews in software engineering – A systematic literature review. Information and Software Technology, 51(1):7–15.
Kong, H.-K., Liu, Z., e Karahalios, K. (2019). Trust and Recall of Information across Varying Degrees of Title-Visualization Misalignment. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, pages 1–13, Glasgow Scotland Uk. ACM.
Li, T., Reiman, K., Agarwal, Y., Cranor, L. F., e Hong, J. I. (2022). Understanding Challenges for Developers to Create Accurate Privacy Nutrition Labels. In CHI Conference on Human Factors in Computing Systems, pages 1–24, New Orleans LA USA. ACM.
Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., e Gebru, T. (2019). Model Cards for Model Reporting. In Proceedings of the Conference on Fairness, Accountability, and Transparency, pages 220–229, Atlanta GA USA. ACM.
Nunes, J. L., Barbosa, G. D. J., De Souza, C. S., e Barbosa, S. D. J. (2024). Using Model Cards for ethical refection on machine learning models: An interview-based study. Journal on Interactive Systems, 15(1):1–19.
Nunes, J. L., Barbosa, G. D. J., de Souza, C. S., Lopes, H., e Barbosa, S. D. J. (2022). Using model cards for ethical refection: A qualitative exploration. In Proceedings of the 21st Brazilian Symposium on Human Factors in Computing Systems, IHC ’22, pages 1–11, New York, NY, USA. Association for Computing Machinery.
Nunes Vilaza, G., Doherty, K., McCashin, D., Coyle, D., Bardram, J., e Barry, M. (2022). A Scoping Review of Ethics Across SIGCHI. In Designing Interactive Systems Conference, pages 137–154, Virtual Event Australia. ACM.
Panagiotidou, G., Poblome, J., Aerts, J., e Vande Moere, A. (2022). Designing a Data Visualisation for Interdisciplinary Scientists. How to Transparently Convey Data Frictions? Computer Supported Cooperative Work (CSCW), 31(4):633–667.
Pandey, A. V., Manivannan, A., Nov, O., Satterthwaite, M., e Bertini, E. (2014). The Persuasive Power of Data Visualization. IEEE Transactions on Visualization and Computer Graphics, 20(12):2211–2220.
Pereira, R., Darin, T., e Silveira, M. S. (2024). GranDIHC-BR: Grand Research Challenges in Human-Computer Interaction in Brazil for 2025-2035. In Proceedings of the XXIII Brazilian Symposium on Human Factors in Computing Systems, pages 1–24, Brasília DF Brazil. ACM.
Pimentel, J. L. (2019). Some Biases in Likert Scaling Usage and its Correction. International Journal of Sciences, 45(1).
Sacha, D., Senaratne, H., Kwon, B. C., Ellis, G., e Keim, D. A. (2016). The Role of Uncertainty, Awareness, and Trust in Visual Analytics. IEEE Transactions on Visualization and Computer Graphics, 22(1):240–249.
Segel, E. e Heer, J. (2010). Narrative Visualization: Telling Stories with Data. IEEE Transactions on Visualization and Computer Graphics, 16(6):1139–1148.
Trans Student Educational Resources (2015). The gender unicorn. Accessed: 2024-08-31.
Verbeek, P.-P. (2008). Morality in design: Design ethics and the morality of technological artifacts. In Philosophy and Design: From Engineering to Architecture, pages 91–103. Springer Netherlands, Dordrecht.
Wall, E., Blaha, L., Paul, C., e Endert, A. (2019). A Formative Study of Interactive Bias Metrics in Visual Analytics Using Anchoring Bias. In Lamas, D., Loizides, F., Nacke, L., Petrie, H., Winckler, M., e Zaphiris, P., editors, Human-Computer Interaction – INTERACT 2019, volume 11747, pages 555–575. Springer International Publishing, Cham.
Wang, Z., Hao, S., e Carpendale, S. (2024). Card-based approach to engage exploring ethics in AI for data visualization. In Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems, Chi Ea ’24, New York, NY, USA. Association for Computing Machinery.
Wanzer, D. L., Azzam, T., Jones, N. D., e Skousen, D. (2021). The role of titles in enhancing data visualization. Evaluation and Program Planning, 84:101896.
Wu, K., Petersen, E., Ahmad, T., Burlinson, D., Tanis, S., e Szafr, D. A. (2021). Understanding Data Accessibility for People with Intellectual and Developmental Disabilities. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, pages 1–16, Yokohama Japan. ACM.
Yan, X., Zhou, Y., Mishra, A., Mishra, H., e Wang, B. (2024). Exploring Visualization for Fairness in AI Education. In 2024 IEEE 17th Pacifc Visualization Conference (PacifcVis), pages 1–10, Tokyo, Japan. IEEE.
Borkin, M. A., Bylinskii, Z., Kim, N. W., Bainbridge, C. M., Yeh, C. S., Borkin, D., Pfster, H., e Oliva, A. (2016). Beyond Memorability: Visualization Recognition and Recall. IEEE Transactions on Visualization and Computer Graphics, 22(1):519–528.
Burns, A., Lee, C., On, T., Xiong, C., Peck, E., e Mahyar, N. (2024). From Invisible to Visible: Impacts of Metadata in Communicative Data Visualization. IEEE Transactions on Visualization and Computer Graphics, 30(7):3427–3443.
Burns, A., On, T., Lee, C., Shapiro, R., Xiong, C., e Mahyar, N. (2021). Making the Invisible Visible: Risks and Benefts of Disclosing Metadata in Visualization. In 2021 IEEE Workshop on Visualization for Social Good (VIS4Good), pages 11–15, New Orleans, LA, USA. IEEE.
Cabrera, A. A., Epperson, W., Hohman, F., Kahng, M., Morgenstern, J., e Chau, D. H. (2019). FAIRVIS: Visual Analytics for Discovering Intersectional Bias in Machine Learning. In 2019 IEEE Conference on Visual Analytics Science and Technology (VAST), pages 46–56, Vancouver, BC, Canada. IEEE.
Cabric, F., Bjarnadóttir, M. V., Ling, M., Rafnsdóttir, G. L., e Isenberg, P. (2024). Eleven years of gender data visualization: A step towards more inclusive gender representation. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, 30(1):10.
Cairo, A. (2019). How Charts Lie: Getting Smarter about Visual Information. W. W. Norton & Company, New York, first edition edition.
Carvalho, L. P., Suzano, J. A., Santoro, F. M., e Oliveira, J. (2022). A meta-scientific broad panorama of ethical aspects in the Brazilian IHC. Journal on Interactive Systems, 13(1):105–126.
Cavanagh, T. (2008). Diverse designing: Sorting out function and intention in artifacts. In Philosophy and Design: From Engineering to Architecture, pages 301–315. Springer Netherlands, Dordrecht.
Chen, M., Floridi, L., e Borgo, R. (2014). What Is Visualization Really For? In Floridi, L. e Illari, P., editors, The Philosophy of Information Quality, volume 358, pages 75–93. Springer International Publishing, Cham.
Chmielinski, K., Newman, S., Taylor, M., Joseph, J., Thomas, K., Yurkofsky, J., e Qiu, Y. C. (2020). The dataset nutrition label (2nd gen): Leveraging context to mitigate harms in artificial intelligence. In NeurIPS Workshop on Dataset Curation and Security.
Coeckelbergh, M. (2020). Artificial Intelligence, Responsibility Attribution, and a Relational Justification of Explainability. Science and Engineering Ethics, 26(4):2051–2068.
Coleti, T. A., Divino, S. B. S., Salgado, A. D. L., Zacarias, R. O., Saraiva, J. D. A. G., Gonçalves, D. A., Morandini, M., e Santos, R. P. D. (2024). GranDIHC-BR 2025-2035 - GC5 - Human-Data Interaction Data Literacy and Usable Privacy. In Proceedings of the XXIII Brazilian Symposium on Human Factors in Computing Systems, pages 1–24, Brasília DF Brazil. ACM.
Correll, M. (2019). Ethical Dimensions of Visualization Research. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, pages 1–13.
Correll, M. e Heer, J. (2017). Black Hat Visualization. In Workshop on Dealing with Cognitive Biases in Visualisations (DECISIVe), IEEE VIS, 2017.
D’Ignazio, C. e Klein, L. F. (2016). Feminist Data Visualization. In Workshop on Visualization for the Digital Humanities at IEEE VIS Conference 2016.
D’Ignazio, C. e Klein, L. F. (2020). Data Feminism. The MIT Press.
Dörk, M., Feng, P., Collins, C., e Carpendale, S. (2013). Critical InfoVis: Exploring the politics of visualization. In CHI ’13 Extended Abstracts on Human Factors in Computing Systems, pages 2189–2198, Paris France. ACM.
Fischer, M. T., Hirsbrunner, S. D., Jentner, W., Miller, M., Keim, D. A., e Helm, P. (2022). Promoting Ethical Awareness in Communication Analysis: Investigating Potentials and Limits of Visual Analytics for Intelligence Applications. In 2022 ACM Conference on Fairness, Accountability, and Transparency, pages 877–889, Seoul Republic of Korea. ACM.
Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Iii, H. D., e Crawford, K. (2021). Datasheets for datasets. Communications of the ACM, 64(12):86–92.
Hepworth, K. J. (2020). Make Me Care: Ethical Visualization for Impact in the Sciences and Data Sciences. In Marcus, A. e Rosenzweig, E., editors, Design, User Experience, and Usability. Interaction Design, volume 12200, pages 385–404. Springer International Publishing, Cham.
Hill, R. L., Kennedy, H., e Gerrard, Y. (2016). Visualizing Junk: Big Data Visualizations and the Need for Feminist Data Studies. Journal of Communication Inquiry, 40(4):331–350.
Holland, S., Hosny, A., Newman, S., Joseph, J., e Chmielinski, K. (2018). The dataset nutrition label: A framework to drive higher data quality standards.
Huey, H., Oey, L. A., Lloyd, H. S., e Fan, J. E. (2023). How do communicative goals guide which data visualizations people think are effective? Proceedings of the Annual Meeting of the Cognitive Science Society, 45(45).
Hullman, J. e Diakopoulos, N. (2011). Visualization Rhetoric: Framing Effects in Narrative Visualization. IEEE Transactions on Visualization and Computer Graphics, 17(12):2231–2240.
Jensen, C. e Potts, C. (2004). Privacy policies as decision-making tools: An evaluation of online privacy notices. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Chi ’04, pages 471–478, New York, NY, USA. Association for Computing Machinery.
Joshi, A., Kale, S., Chandel, S., e Pal, D. (2015). Likert Scale: Explored and Explained. British Journal of Applied Science & Technology, 7(4):396–403.
Kelley, P. G., Bresee, J., Cranor, L. F., e Reeder, R. W. (2009). A "nutrition label" for privacy. In Proceedings of the 5th Symposium on Usable Privacy and Security, pages 1–12, Mountain View California USA. ACM.
Kelley, P. G., Cranor, L. F., e Sadeh, N. (2013). Privacy as part of the app decision-making process. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pages 3393–3402, Paris France. ACM.
Kitchenham, B., Pearl Brereton, O., Budgen, D., Turner, M., Bailey, J., e Linkman, S. (2009). Systematic literature reviews in software engineering – A systematic literature review. Information and Software Technology, 51(1):7–15.
Kong, H.-K., Liu, Z., e Karahalios, K. (2019). Trust and Recall of Information across Varying Degrees of Title-Visualization Misalignment. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, pages 1–13, Glasgow Scotland Uk. ACM.
Li, T., Reiman, K., Agarwal, Y., Cranor, L. F., e Hong, J. I. (2022). Understanding Challenges for Developers to Create Accurate Privacy Nutrition Labels. In CHI Conference on Human Factors in Computing Systems, pages 1–24, New Orleans LA USA. ACM.
Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., e Gebru, T. (2019). Model Cards for Model Reporting. In Proceedings of the Conference on Fairness, Accountability, and Transparency, pages 220–229, Atlanta GA USA. ACM.
Nunes, J. L., Barbosa, G. D. J., De Souza, C. S., e Barbosa, S. D. J. (2024). Using Model Cards for ethical refection on machine learning models: An interview-based study. Journal on Interactive Systems, 15(1):1–19.
Nunes, J. L., Barbosa, G. D. J., de Souza, C. S., Lopes, H., e Barbosa, S. D. J. (2022). Using model cards for ethical refection: A qualitative exploration. In Proceedings of the 21st Brazilian Symposium on Human Factors in Computing Systems, IHC ’22, pages 1–11, New York, NY, USA. Association for Computing Machinery.
Nunes Vilaza, G., Doherty, K., McCashin, D., Coyle, D., Bardram, J., e Barry, M. (2022). A Scoping Review of Ethics Across SIGCHI. In Designing Interactive Systems Conference, pages 137–154, Virtual Event Australia. ACM.
Panagiotidou, G., Poblome, J., Aerts, J., e Vande Moere, A. (2022). Designing a Data Visualisation for Interdisciplinary Scientists. How to Transparently Convey Data Frictions? Computer Supported Cooperative Work (CSCW), 31(4):633–667.
Pandey, A. V., Manivannan, A., Nov, O., Satterthwaite, M., e Bertini, E. (2014). The Persuasive Power of Data Visualization. IEEE Transactions on Visualization and Computer Graphics, 20(12):2211–2220.
Pereira, R., Darin, T., e Silveira, M. S. (2024). GranDIHC-BR: Grand Research Challenges in Human-Computer Interaction in Brazil for 2025-2035. In Proceedings of the XXIII Brazilian Symposium on Human Factors in Computing Systems, pages 1–24, Brasília DF Brazil. ACM.
Pimentel, J. L. (2019). Some Biases in Likert Scaling Usage and its Correction. International Journal of Sciences, 45(1).
Sacha, D., Senaratne, H., Kwon, B. C., Ellis, G., e Keim, D. A. (2016). The Role of Uncertainty, Awareness, and Trust in Visual Analytics. IEEE Transactions on Visualization and Computer Graphics, 22(1):240–249.
Segel, E. e Heer, J. (2010). Narrative Visualization: Telling Stories with Data. IEEE Transactions on Visualization and Computer Graphics, 16(6):1139–1148.
Trans Student Educational Resources (2015). The gender unicorn. Accessed: 2024-08-31.
Verbeek, P.-P. (2008). Morality in design: Design ethics and the morality of technological artifacts. In Philosophy and Design: From Engineering to Architecture, pages 91–103. Springer Netherlands, Dordrecht.
Wall, E., Blaha, L., Paul, C., e Endert, A. (2019). A Formative Study of Interactive Bias Metrics in Visual Analytics Using Anchoring Bias. In Lamas, D., Loizides, F., Nacke, L., Petrie, H., Winckler, M., e Zaphiris, P., editors, Human-Computer Interaction – INTERACT 2019, volume 11747, pages 555–575. Springer International Publishing, Cham.
Wang, Z., Hao, S., e Carpendale, S. (2024). Card-based approach to engage exploring ethics in AI for data visualization. In Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems, Chi Ea ’24, New York, NY, USA. Association for Computing Machinery.
Wanzer, D. L., Azzam, T., Jones, N. D., e Skousen, D. (2021). The role of titles in enhancing data visualization. Evaluation and Program Planning, 84:101896.
Wu, K., Petersen, E., Ahmad, T., Burlinson, D., Tanis, S., e Szafr, D. A. (2021). Understanding Data Accessibility for People with Intellectual and Developmental Disabilities. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, pages 1–16, Yokohama Japan. ACM.
Yan, X., Zhou, Y., Mishra, A., Mishra, H., e Wang, B. (2024). Exploring Visualization for Fairness in AI Education. In 2024 IEEE 17th Pacifc Visualization Conference (PacifcVis), pages 1–10, Tokyo, Japan. IEEE.
Publicado
08/09/2025
Como Citar
JUNGER, Ana Caroline Ferreira; BARBOSA, Simone Diniz Junqueira.
Understanding the perceived importance of disclosing ethical concerns underlying data visualizations. In: SIMPÓSIO BRASILEIRO SOBRE FATORES HUMANOS EM SISTEMAS COMPUTACIONAIS (IHC), 24. , 2025, Belo Horizonte/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 1439-1465.
DOI: https://doi.org/10.5753/ihc.2025.10920.
