Annals of Computer Science and Information Systems, Volume 15

DOI: http://dx.doi.org/10.15439/2018F174

Citation: Proceedings of the 2018 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 15, pages 897–906 (2018)

Full text

Abstract. The growing practice of accumulating personal data to generate predictions about users, leverages the need for mechanisms that allow people a more effective control of their data. An emerging field of studies called Human-Data Interaction (HDI), proposes the inclusion of human at the center of the data flow, providing mechanisms to citizens to interact explicitly with the collected data. Researches in HDI have discussed ways to offer Transparency Enhancing Tools (TETs), i.e., tools that support people on HDI issues related to privacy and personal data protection. Many works conducted about TETs focuses on usability issues, exploring aspects such as efficiency, user satisfaction and ease of learning. In this work, on the other hand, we aim to assess the communicability of HDI mechanisms in TETs. Hence, we applyed the Semiotic Inspection Method (SIM) to investigate if and how HDI concepts are applied in two different TETs used for personal data management. We triangulated results from the study with findings from another investigation about communicability issues carried out in the same domain, but by observing and interviewing users.

References

1. H. Hornung, R. Pereira, M. Baranauskas, and K. Liu, “Challenges for human-data interaction–a semiotic perspective,” in International Conference on Human-Computer Interaction. Springer, 2015, pp. 37–
2. J. Han, H. Ding, C. Qian, W. Xi, Z. Wang, Z. Jiang, L. Shangguan, and J. Zhao, “Cbid: A customer behavior identification system using passive tags,” IEEE/ACM Transactions on Networking, vol. 24, no. 5, pp. 2885–2898, 2016.
3. C. Meurisch, U. Naeem, M. A. Azam, F. Janssen, B. Schmidt, and M. Mühlhäuser, “Smarticipation: intelligent personal guidance of human behavior utilizing anticipatory models,” in Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct. ACM, 2016, pp. 1227–1230.
4. A. Doryab, M. Frost, M. Faurholt-Jepsen, L. V. Kessing, and J. E. Bardram, “Impact factor analysis: combining prediction with parameter ranking to reveal the impact of behavior on health outcome,” Personal and Ubiquitous Computing, vol. 19, no. 2, pp. 355–365, 2015.
5. F. Zhang, N. J. Yuan, K. Zheng, D. Lian, X. Xie, and Y. Rui, “Mining consumer impulsivity from offline and online behavior,” in Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 2015, pp. 1281–1292.
6. R. Mortier, H. Haddadi, T. Henderson, D. McAuley, and J. Crowcroft, “Challenges & opportunities in human-data interaction,” University of Cambridge, Computer Laboratory, 2013.
7. R. Mortier, J. Crowcroft, D. McAuley, H. Haddadi, and T. Henderson, “Human-data interaction: The human face of the data-drivem society,” 2014.
8. E. W. Ritter and S. J. Rigo, “Fitdata: A system for monitoring physical activity based on mobile devices,” in Proceedings of the XII Brazilian Symposium - Volume 1, ser. SBSI 2016. Porto Alegre, Brazil: Brazilian Computer Society, 2016. ISBN 978-85-7669-317-8 pp. 72:550–72:557.
9. C. Buck and S. Burster, “App information privacy concerns,” AIS Electronic Library - Americas Conference on Information Systems, 2017.
10. C. Buck, “Stop disclosing personal data about your future self,” AIS Electronic Library - Americas Conference on Information Systems, 2017.
11. J. Angulo, S. Fischer-Hübner, T. Pulls, and E. Wästlund, “Usable transparency with the data track: a tool for visualizing data disclosures,” in Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems. ACM, 2015, pp. 1803–1808.
12. C. Bier, K. Kühne, and J. Beyerer, “Privacyinsight: the next generation privacy dashboard,” in Annual Privacy Forum. Springer, 2016, pp. 135–152.
13. J. Siljee, “Privacy transparency patterns,” in Proceedings of the 20th European Conference on Pattern Languages of Programs. ACM, 2015, p. 52.
14. C. S. De Souza, The semiotic engineering of human-computer interaction. MIT press, 2005.
15. C. F. and Leitão and C. S. De Souza, “Semiotic engineering methods for scientific research in hci,” Synthesis Lectures on Human-Centered Informatics, vol. 2, no. 1, pp. 1–122, 2009.
16. F. Cafaro, “Using embodied allegories to design gesture suites for human-data interaction,” in Proceedings of the 2012 ACM Conference on Ubiquitous Computing. ACM, 2012, pp. 560–563.
17. A. Crabtree and R. Mortier, “Human data interaction: historical lessons from social studies and cscw,” in Proceedings of the 14th European Conference on Computer Supported Cooperative Work, 19-23 September 2015, Oslo, Norway. Springer, 2015, pp. 3–21.
18. C. S. de Souza, C. F. Leitão, R. O. Prates, and E. J. da Silva, “The semiotic inspection method,” in Proceedings of VII Brazilian symposium on Human factors in computing systems. ACM, 2006, pp. 148–157.
19. C. S. de Souza, C. F. Leitão, R. O. Prates, S. A. Bim, and E. J. da Silva, “Can inspection methods generate valid new knowledge in hci? the case of semiotic inspection,” International Journal of Human-Computer Studies, vol. 68, no. 1-2, pp. 22–40, 2010.
20. R. O. Prates, C. S. de Souza, and S. D. Barbosa, “Methods and tools: a method for evaluating the communicability of user interfaces,” interactions, vol. 7, no. 1, pp. 31–38, 2000.
21. S. Lewis, “Qualitative inquiry and research design: Choosing among five approaches,” Health promotion practice, vol. 16, no. 4, pp. 473–475, 2015.
22. “Google myactivity,” https://myactivity.google.com/, acessado em 12/11/2017.
23. “Privacy badger,” https://www.eff.org/privacybadger, acessado em 10/01/2018.
24. N. K. Denzin and Y. S. Lincoln, The landscape of qualitative research. Sage, 2008, vol. 1.
25. J. W. Creswell and J. D. Creswell, Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications, 2009. 48.