Re-Shape: A Method to Teach Data Ethics for Data Science Education

Data has become central to the technologies and services that human-computer interaction (HCI) designers make, and the ethical use of data in and through these technologies should be given critical attention throughout the design process. However, there is little research on ethics education in computer science that explicitly addresses data ethics. We present and analyze Re-Shape, a method to teach students about the ethical implications of data collection and use. Re-Shape, as part of an educational environment, builds upon the idea of cultivating care and allows students to collect, process, and visualize their physical movement data in ways that support critical reflection and coordinated classroom activities about data, data privacy, and human-centered systems for data science. We also use a case study of Re-Shape in an undergraduate computer science course to explore prospects and limitations of instructional designs and educational technology such as Re-Shape that leverage personal data to teach data ethics.

[1]  Alexandra Branzan Albu,et al.  Teaching Computer Vision and Its Societal Effects: A Look at Privacy and Security Issues from the Students’ Perspective , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[2]  Mario Romero,et al.  Situational Ethics: Re-thinking Approaches to Formal Ethics Requirements for Human-Computer Interaction , 2015, CHI.

[3]  B. Shapiro,et al.  Using the interaction geography slicer to visualize New York City Stop & Frisk , 2017, 2017 IEEE VIS Arts Program (VISAP).

[4]  E. Brink,et al.  Constructing grounded theory : A practical guide through qualitative analysis , 2006 .

[5]  Ellen W. Zegura,et al.  Care and the Practice of Data Science for Social Good , 2018, COMPASS.

[6]  Srinjita Bhaduri,et al.  Ethics Education in Context: A Case Study of Novel Ethics Activities for the CS Classroom , 2018, SIGCSE.

[7]  Nancy J. Hirschmann,et al.  Moral Boundaries: A Political Argument for an Ethic of Care. By Joan Tronto. New York: Routledge, 1993. 242 pp. $173.00 (hardcover), $53.95 (paperback). , 2018, Politics & Gender.

[8]  Victor R. Lee.  The Quantified Self (QS) Movement and Some Emerging Opportunities for the Educational Technology Field , 2013 .

[9]  Mark Guzdial,et al.  Helping Social Studies Teachers to Design Learning Experiences Around Data: Participatory Design for New Teacher-Centric Programming Languages , 2019, ICER.

[10]  Ellen W. Zegura,et al.  Collaborative Data Work Towards a Caring Democracy , 2019, Proc. ACM Hum. Comput. Interact..

[11]  Carl F. DiSalvo,et al.  Data, Design and Civics: An Exploratory Study of Civic Tech , 2016, CHI.

[12]  L. Lyons,et al.  Examining Spontaneous Perspective Taking and Fluid Self-to-Data Relationships in Informal Open-Ended Data Exploration , 2019, Journal of the Learning Sciences.

[13]  A. Fotopoulou.  Understanding citizen data practices from a feminist perspective , 2019, Citizen Media and Practice.

[14]  Deborah Lupton,et al.  How do data come to matter? Living and becoming with personal data , 2018, Big Data & Society.

[15]  Mei-Po Kwan,et al.  From oral histories to visual narratives: re-presenting the post-September 11 experiences of the Muslim women in the USA , 2008 .

[16]  Betsy DiSalvo,et al.  Design in the HCI Classroom: Setting a Research Agenda , 2019, Conference on Designing Interactive Systems.

[17]  Colin M. Gray,et al.  Ethical Mediation in UX Practice , 2019, CHI.

[18]  Nicholas Mattei,et al.  How to teach computer ethics through science fiction , 2018, Commun. ACM.

[19]  Tapan S. Parikh,et al.  Local ground: A toolkit supporting meta-representational competence in data science , 2014 .

[20]  Victor R. Lee.  Personal Analytics Explorations to Support Youth Learning , 2018 .

[21]  Tapan S. Parikh,et al.  Cultivating Care through Ambiguity: Lessons from a Service Learning Course , 2019, CHI.

[22]  Simon Sheikh.  The Production of Space , 1996 .

[23]  Anne Gates Applin.  A learner-centered approach to teaching ethics in computing , 2006, SIGCSE '06.

[24]  Jason C. Yip,et al.  Laughing is Scary, but Farting is Cute: A Conceptual Model of Children's Perspectives of Creepy Technologies , 2019, CHI.

[25]  Ben Shneiderman,et al.  The eyes have it: a task by data type taxonomy for information visualizations , 1996, Proceedings 1996 IEEE Symposium on Visual Languages.

[26]  Rogers Hall,et al.  Counter-Mapping the Neighborhood on Bicycles: Mobilizing Youth to Reimagine the City , 2013, Technol. Knowl. Learn..

[27]  Jan Gulliksen,et al.  User-centered System Design , 2011 .

[28]  David A. Owens,et al.  Developing & using interaction geography in a museum , 2017, Int. J. Comput. Support. Collab. Learn..

[29]  Casey Fiesler,et al.  What Do We Teach When We Teach Tech Ethics?: A Syllabi Analysis , 2020, SIGCSE.

[30]  Naveena Karusala,et al.  Care as a Resource in Underserved Learning Environments , 2017, Proc. ACM Hum. Comput. Interact..

[31]  N. Noddings.  Educating Moral People: A Caring Alternative to Character Education , 2002 .

[32]  R. Benjamin Shapiro,et al.  Youth Learning Machine Learning through Building Models of Athletic Moves , 2019, IDC.

[33]  Laurie H. Rubel,et al.  Teaching Mathematics for Spatial Justice: An Investigation of the Lottery , 2016 .

[34]  Tom Jenkins,et al.  Making public things: how HCI design can express matters of concern , 2014, CHI.

[35]  Sayamindu Dasgupta,et al.  Youth Perspectives on Critical Data Literacies , 2017, CHI.

[36]  Tony Doyle,et al.  Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy , 2017, Inf. Soc..

[37]  N. Montessori.  Seeking Spatial Justice , 2012 .

[38]  R. Benjamin Shapiro,et al.  Sports and machine learning: How young people can use data from their own bodies to learn about machine learning , 2019, XRDS.

[39]  Mary Goodwin,et al.  Effective incorporation of ethics into courses that focus on programming , 2005, SIGCSE '05.

[40]  R. Pea.  User Centered System Design: New Perspectives on Human-Computer Interaction , 1987 .

[41]  María Puig de la Bellacasa.  Matters of care in technoscience: Assembling neglected things , 2011, Social studies of science.

[42]  T. Philip,et al.  Becoming Racially Literate About Data and Data-Literate About Race: Data Visualizations in the Classroom as a Site of Racial-Ideological Micro-Contestations , 2016 .

[43]  Leilah Lyons,et al.  Interpreting data from within: supporting humandata interaction in museum exhibits through perspective taking , 2014, IDC.

[44]  Robert Heckman,et al.  Integrating Ethics within Machine Learning Courses , 2019, ACM Trans. Comput. Educ..

[45]  Ben Rydal Shapiro.  Using Space Time Visualization in Learning Environment Design , 2017, CHI Extended Abstracts.

[46]  Rogers Hall,et al.  Personal Curation in a Museum , 2018, Proc. ACM Hum. Comput. Interact..

[47]  David Nemer,et al.  Sociotechnical Systems of Care , 2018, CSCW Companion.

[48]  A. Strauss,et al.  The discovery of grounded theory: strategies for qualitative research aldine de gruyter , 1968 .

[49]  Silvia Lindtner,et al.  Caring through Data: Attending to the Social and Emotional Experiences of Health Datafication , 2017, CSCW.

[50]  Phoebe Sengers,et al.  Expanding and Refining Design and Criticality in HCI , 2015, CHI.

[51]  Casey Reas,et al.  Processing: a programming handbook for visual designers and artists , 2007 .

[52]  Kalani Craig,et al.  Analog Tools in Digital History Classrooms: An Activity-Theory Case Study of Learning Opportunities in Digital Humanities , 2017 .

[53]  Erik Duval,et al.  Unfolding - A Library for Interactive Maps , 2013, SouthCHI.

[54]  Shaowen Bardzell,et al.  The Proper Care and Feeding of Hackerspaces: Care Ethics and Cultures of Making , 2015, CHI.