On October 17, 2022, the Workshop on Interactions with Data Experiences (WIDE) was held at the XXI Brazilian Symposium on Human Factors in Computational Systems, in the city of Diamantina (IHC 2022) — Minas Gerais. The workshop was organized in collaboration with the researchers Luciana Brito, Juliana França, Angélica Dias and Adriana Vivacqua (Universidade Federal do Rio de Janeiro), Luiz Morais (Inria) and Caroline Queiroz (Universidade Federal dos Vales de Jequitinhonha e Mucuri). The program committee was formed by the researchers from 3 continents.
Some of the previous HCI workshops that we drew on to create our workshop were the Chi workshops [1], [5], [9] and [10].
The objective of the workshop was to stimulate a discussion on human-data interaction, supporting the formation of a community around the subject, and tracing paths for future research, in addition to advancing the literature on human-data interaction and fostering partnerships and new scientific connections on the subject in Brazil [3].
The workshop elaboration began when the group was created to collaborate in writing the proposal for submission to the IHC 2022, which occurred through the search for researchers in the area of human-data interaction belonging to the Brazilian and international HCI community. For the submission of the proposal, the call to authors was first defined for the submission of works articulating the areas of HCI and human-data interaction, as well as innovative projects related to the theme, mainly related to accessibility and social applications.
The topics of interest for the workshop were defined were: i. data literacy; ii. physicalization of data; iii. data visualization; iv. explanations of data-driven processes; v. Data Science involving humans; vi. accessibility of data and visualizations; vii. human-data interaction in social contexts; viii. human-data interaction in educational contexts; ix. human factors in the context of human-data interaction; x. theories of human data interaction; and xi. innovative implementation techniques of human-data interaction.
After defining the topics of interest, the program committee was defined through the search for Brazilian researchers who work within fields of study related to the topics of interest in the workshop. After this, WIDE website was built with all the information for the submission of proposals, as well as other information related to the event. As a last step, the workshop schedule was defined, with the activities that would be carried out, as well as the acquisition of the necessary resources for the event (stationery materials, projector, sound system, and adequate physical space).
WIDE took place throughout the afternoon and had the participation of 20 researchers, including professors, master’s, doctoral and undergraduate students. The workshop started with the presentation of the planned program right after the presentation of each of the 4 selected articles, followed by 1 stage of collaborative activity with participants divided into 2 large heterogeneous groups, each with approximately half the number of participants in the event and divided according to individual choice, in which they should survey proposals for the composition of an agenda for HDI research for the next 5 years, taking as inspiration the presentations of the research approved for the workshop.
Each group initially had 1 hour and 30 minutes for internal brainstorming. After this step, the groups met for 30 minutes in a large group to consolidate a proposal for a single agenda and present the results.
Among the articles selected for presentation, [7] presents a discussion on the concepts and challenges of Data Transparency, reports activities of its research group to make transparency perceptible and controllable for data subjects and even presents a research agenda for the following years; [6] evaluated the usability of an information system on Brazilian dams composed of a dashboard and an information filter, mapping problems in the interaction to improve the system; [2] designed a qualitative scale of data literacy, through Design Science Research, capable of assessing students in 60 skills and competencies organized into 7 categories ranging from data collection to making informed decisions; and [8] presented a pictorial analysis of data visualizations released by Brazilian state governments during the COVID-19 pandemic with the objective of contributing to the identification of graphics with the potential to be misinterpreted or poorly understood because they do not follow basic guidelines in the area of data visualization.
As a result of the workshop, an agenda for research in Human-Data Interaction was obtained for the next 5 years, consisting of 8 priority development axes for the area accompanied by concerns/provocations to feed the development of solutions:
1. Accessibility — how to adjust the understanding of information by users with autism and Down syndrome?
2. Design and UX — transparency of personal data, design patterns, dark patterns, and color patterns for data visualization.
3. Transdisciplinarity — transdisciplinarity in teaching data literacy, transdisciplinarity in data visualization, small HDI community, and few works in the field of HDI.
4. Data representation — little knowledge about how to represent data, how to make users understand what we want to represent? encouraging reading, information optimization, use of tools, ontology, and anonymization.
5. Environment — smart energy (GRID), smart government.
6. Storytelling — how to identify the veracity of information? What to show in storytelling? What to do with fake news, deep fake? Source of information/what is true? Narrative and storytelling.
7. Metaverse — machine learning and artificial intelligence.
8. Legal challenges — Brazilian “General Personal Data Protection Law (LGPD)” and privacy, protection/consent, Brazilian “Access to Information Law”/public information, Brazilian Child and Adolescent Statute (ECA), Brazilian Statute of the Elderly, Brazilian Statute of Persons with Disabilities, Brazilian Constitution.
Furthermore, essential research relationships were consolidated, and a collaboration network was built between researchers in HDI.
We built an agenda from the workshop results, taking into account the sustainable Development Goals of the United Nations. The sustainability objectives are a global call to action to end poverty, protect the environment and climate, and ensure that people everywhere enjoy peace and prosperity. In this sense, we present some recommendations and challenges to the IHD field for the next 5 years.
Data for everyone | Sustainable objectives related: 5, 10.
As our first and more significant challenge, we have much work to do improving data literacy, empowering people to use data, and considering neurodiversity in the learning approaches and data visualizations.
1. Improve data literacy at all levels of education.
2. Empower people for data use, considering the importance of providing data literacy for diverse ages, genders, sexual orientations, social vulnerabilities, and the intersection of different ways of oppression.
3. Improve data literacy considering neurodiversity.
Design and UX | Sustainable objective related: 16
In this research topic, it is essential to understand how human data interaction can help us improve design patterns to understand and avoid dark patterns and better use color patterns for the audience. We also need to fight against misinformation and continue our research with data visualization, mainly to show social issues and fight against fascism.
4. Improve laws, people’s knowledge, and advocacy related to the protection and transparency of personal data.
5. Improve research about design patterns.
6. Promote education and research about dark patterns and their impact on human data rights, data literacy, and democracy.
7. Promote education and research about color patterns and their impact on human data rights, data literacy, and accessibility.
8. Promote education and research about misinformation and its impact on democracy and freedom. 9. Promote research and education about data visualization in all its complexity and multidisciplinarity.
Transdisciplinarity and Collaboration | Sustainable objectives related: 17
We need to encourage transdisciplinarity in teaching data literacy and data visualizations. Also, foster HDI community growth, collaboration, and new research through institutional partnerships.
10. Encourage transdisciplinarity in teaching data visualization.
11. Encourage transdisciplinarity in data visualizations.
12. Foster HDI community growth.
13. Foster new research about collaborating through data to create services and products for marginalized communities.
14. Encourage human-data interaction partnerships between educational, research, and industry institutions.
Data Representation | Sustainable objectives related: 4, 16
15. Foster knowledge about how to represent data.
16. Foster the interpretation and understanding of data representations.
17. Encourage data reading.
18. Encourage information optimization using tools and ontologies.
19. Educate on ethics in data representation, especially regarding data anonymization.
Environment | Sustainable objectives related: 1, 2, 3, 6, 7, 8, 11, 12, 13, 14, 15
We have to encourage the use of data for the consolidation of smart grids and smart government considering political commitment, clear governance, legal agility, digital awareness, IT infrastructure, and human workers in these processes.
20. Encourage the consolidation of smart grids through transparent and controllable energy transmission and distribution systems.
21. Encourage the consolidation of smart government from an institutional, organizational, and strategic/leadership point of view, considering political commitment, clear governance, legal agility, digital awareness, IT infrastructure, natural processes, situations of emergencies, and capabilities, values and human roles in this processes.
Storytelling | Sustainable objectives related: 4, 16
We strongly recommend joining efforts in identifying the veracity of information and empowering people to read and write storytelling and differentiate storytelling from narratives made from a particular point of view.
22. Join efforts in the search for ways to identify the veracity of information.
23. Clarifying the educational steps necessary to carry out storytelling with data.
24. Develop means to identify fake news, deep fake, and other forms of lying through data.
25. Enable the necessary knowledge so that people know how to differentiate storytelling from a narrative, considering the narrative as storytelling that promotes a particular point of view.
Metaverse | Sustainable objective related: 9
We have to incentive the ethical use of metaverse to overcome geographic barriers for equitable access to goods, services, and knowledge produced by the development.
26. encourage the use of data in virtual environments ethically with the aim of innovation in the most varied areas of importance for social and economic development and the overcoming of geographic barriers for equitable access to goods, services, and knowledge produced by development
Legal Challenges | Sustainable objective related: 16
The legal challenges are in the first place: promote knowledge of the laws, promote data compliance at the institutions, encourage advocacy initiatives, and provide opportunities to monitor and verify the actions of public management in the execution of public data policies.
27. Promote knowledge of the laws that regulate and control the use of data in national and international territories.
28. Promote compliance with standards, laws and guidelines, and data policies in national institutions.
29. Encourage participation in advocacy initiatives carried out by the population and civil society entities to formulate data policies and allocate public resources for data science as a means for decision-making on issues of public interest.
30. Provide opportunities for society to monitor and verify the actions of public management in the execution of public data policies, evaluating objectives, processes, and results.
For the future
Based on the broad participation and involvement of the HCI community with the workshop programming, as well as the quality of the research evaluated and the applicability of the results obtained, it is intended to continue the workshop in new editions, expanding the interaction between researchers in HDI in Brazil and around the world. Therefore, the expansion of research in the thematic axes and recommendations indicated in the agenda obtained for the next 5 years is encouraged, as well as the investigation of new thematic axes and recommendations that complement and improve the quality of the work already done.
References
[1] Madeline Balaam, Lone Koefoed Hansen, Catherine D’Ignazio, Emma Simpson, Teresa Almeida, Stacey Kuznetsov, Mike Catt, and Marie L. J. Søndergaard. 2017. Hacking Women’s Health. In Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA ‘17). Association for Computing Machinery, New York, NY, USA, 476–483. https://doi.org/10.1145/3027063.3027085.
[2] Luciana S. Brito, Juliana B. S. França, Angélica F. S. Dias, and Adriana S. Vivacqua. 2022. Design de uma Escala para Avaliação de Literacia de Dados. Anais do I Workshop Investigações em Interação Humano-Dados (WIDE 2022), 7–12. DOI: https://doi.org/10.5753/wide.2022.227509.
[3] Luciana Brito, Juliana França, Luiz Morais, Caroline Queiroz, Angélica Dias, and Adriana Vivacqua. 2022. Workshop Investigações em Interação Humano-Dados — WIDE. In Anais Estendidos do XXI Simpósio Brasileiro de Fatores Humanos em Sistemas Computacionais, outubro 17, 2022, Diamantina, Brasil. SBC, Porto Alegre, Brasil, 5–8. DOI: https://doi.org/10.5753/ihc_estendido.2022.224943.
[4] Luciana Brito, Juliana França, Luiz Morais, Caroline Queiroz, Angélica Dias, and Adriana Vivacqua. WIDE: Workshop on Interactions with Data Experiences. WIDE: Workshop on Interactions with Data Experiences. Retrieved January 4, 2023 from https://sites.google.com/view/wide2022/p%C3%A1gina-inicial.
[5] Anke M. Brock, Jessica Cauchard, Markus Funk, Jérémie Garcia, Mohamed Khamis, and Matjaž Kljun. 2019. IHDI: International Workshop on Human-Drone Interaction. In Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems (CHI EA ‘19). Association for Computing Machinery, New York, NY, USA, Paper W01, 1–7. https://doi.org/10.1145/3290607.3299001.
[6] Bruna Capeleti, André Freire, Caroline Santos, and Jaqueline Souza. 2022. Interação Humano-Dados: Análise de Dados para Segurança de Barragens Brasileiras. In Anais do I Workshop Investigações em Interação Humano-Dados, outubro 17, 2022, Diamantina/MG, Brasil. SBC, Porto Alegre, Brasil, 13–19. DOI: https://doi.org/10.5753/wide.2022.227500.
[7] Thiago Coleti, Pedro Corrêa, Marcelo Morandini, and Lucia Filgueiras. 2022. Desafios e propostas para Transparência de Dados Pessoais com foco nos titulares dos dados. In Anais do I Workshop Investigações em Interação Humano-Dados, outubro 17, 2022, Diamantina/MG, Brasil. SBC, Porto Alegre, Brasil, 1–6. DOI: https://doi.org/10.5753/wide.2022.227498.
[8] Rodrigo Oliveira, Claudia Cappelli, and Jonice Oliveira. 2022. Uma análise da compreensibilidade das visualizações de dados durante a pandemia de COVID-19 no Brasil. In Anais do I Workshop Investigações em Interação Humano-Dados, outubro 17, 2022, Diamantina/MG, Brasil. SBC, Porto Alegre, Brasil, 20–25. DOI: https://doi.org/10.5753/wide.2022.227543.
[9] Bruce M. McLaren, Jodi Asbell-Clarke, and Jessica Hammer. 2018. CHI 2018 Workshop: Data-Driven Educational Game Design. In Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems (CHI EA ‘18). Association for Computing Machinery, New York, NY, USA, Paper W08, 1–8. https://doi.org/10.1145/3170427.3170637.
[10]Rachel Charlotte Smith, Heike Winschiers-Theophilus, Daria Loi, Rogério Abreu de Paula, Asnath Paula Kambunga, Marly Muudeni Samuel, and Tariq Zaman. 2021. Decolonizing Design Practices: Towards Pluriversality. In Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems (CHI EA ‘21). Association for Computing Machinery, New York, NY, USA, Article 83, 1–5. https://doi.org/10.1145/3411763.3441334.
