PhD Course - Big Data and Ethics

Organizers:
Christopher Gad and Irina Shklovski, IT University of Copenhagen

Lecturers:
Geoffrey Bowker, Professor UC Irvine, Visiting Velux professor at the IT University of Copenhagen

Alison Powell, Assistant Professor, London School of Economics and Political Science

Rachel Douglas–Jones, Assistant Professor, IT University of Copenhagen

Dates of the course:
Monday May 23 2016 - Tuesday May 24 2016

Course description:

This PhD course will explore ethical questions that have emerged in debates about Big Data approaches, processes and implications. The proliferation of personal and impersonal data poses new challenges to ethical questions and moral dilemmas faced by those that produce data and those that collect and use it. Todays’ researchers must address the challenges to traditional assumptions about individuality, free will and power that emerge in applications of big data in research and practice. At first glance the very largeness of the datasets involved makes the individuals whose data makes up the datasets of limited epistemological importance. After all, what is one person in a dataset of hundreds of thousands or millions of data points. Thus the requirement of real informed consent is impractical and even impossible given the scale of the datasets and the relatively unpredictable nature of potential uses of these data. Such changes raise questions for the responsible research and innovation effort across the EU.

This course proposes that topics central to ethical discussions such as research conduct, social good and avoidance of ethical pitfalls are still highly important. However, today’s researchers must also find avenues in their research through which to reconsider traditional ethical assumptions in the changing field of large data sets and shifts in potential for power among stakeholders. We invite essays reflecting on two or more of the listed topics that relate the readings to specific ethical discussions or issues encountered in the course of fieldwork and data collection experienced in students’ own research. We furthermore welcome essays that consider the challenges of interrogating, engaging and managing ethical issues in practice, including essays about how ethics are done in practice or essays that are critical of the notion of ethics itself.

Students will be organized into thematic groups based on their papers. Each will be expected to act as a discussant for another paper, and will receive comments from a visiting or local professor.

Deadlines and how to sign up:

*March 1st:  Submission of short bio of 150 words and an abstract of 200 words.

The bio should include your university department and degree program, thesis topic area and keywords, and please state whether you are in the beginning, in the middle or the final stage of your project.

Please submit your bio and abstract to Christopher Gad: chga@itu.dk, stating “Big data and Ethics - Application” in the subject line.

The abstract should consist in a short description of the PhD candidates work in relation to the overall course theme and state why the course is interesting to their project.

*March 15th:  Notice of acceptance to the course

*May 13th: Submission of paper of 5-7 pages. The paper should primarily discuss the student’s own work in relation to the overall theme and relevant subthemes and parts of the syllabus. You do not need to relate to all themes and texts. Overall the syllabus functions as a shared resource for discussions during the course.

Please notice that the number of attendance is limited to 16 and that the selection of applicants will be based on relevance for the individual PhD projects and ensuring diversity amongst participants.

Please also notice that the course is free to attend but the IT-University does not offer travel funding and that you will need to buy your own lunch and dinner (if you attend). More information on this will follow with the notice of acceptance.


Preliminary Programme:

Monday may 23:

9:00 – 9:30

Coffee & Tea 

 

9:30 – 10:00

Welcome, practical information and presentation round

 

10.00– 11:30

Key note presentation/ Geof Bowker

 

11:30 – 12:30

Lunch Break*

 

12:30– 14:50

Paper session 1

 

14:50 –15:00

Break

 

15:00-16:00

What can big data learn from research ethics /Rachel Douglas-Jones

 

16:00-16:10

break

 

16:10-17:30

Paper session 2

 

17:30-18:00

Voluntary Activity organized by student helper/local PhD students.  Dinner for those who want to

 

Tuesday May 24:

 

9:00-9:30

Coffee & Tea

 

9:30 – 10:50

Paper session 3

 

10:50 -11.00

Break

 

11:00 – 12:00

Alison Powell

 

12:00 – 13:00

Lunch Break*

 

13:00 – 14:20

Paper session 4

 

14:20-14:40

Break

 

14:40 – 15:20

Observations during the course & Emerging challenges & concerns in Big Data & Ethics/ Local Professor

 

15:20 15:30

Break (if necessary)

 

15:30-16:15

Plenum discussion, lessons learned, comments etc.

 

18:30

Course Dinner at Restaurant

 

Prerequisites:
The course is open to all PhD students within IT, but will mostly be aimed at students doing qualitative or multiple methods studies of (digital) technologies especially in the area of big data - A background in ethnography, anthropology, sociology, information studies, informatics, information systems, qualitative approaches in software development or computer science, CSCW, HCI, or science and technology studies is therefore an advantage.

Preparation:
Syllabus reading, watch video materials, essay preparation, presentation in relation to course topics, prepare feedback to another participant.

Credits:
3.5 ECTS

READING LIST (subject to adjustments until notice of acceptance May 15th):

Computer ethics:

  • Floridi L (2013) Distributed morality in an information society. Science and Engineering Ethics 19(3): 727–743
  • Moor JH (1985) What is computer ethics? Metaphilosophy 16(4): 266–275.
  • Noorman M (2012) Computing and moral responsibility. In: Zalta EN (ed) The Stanford Encyclopedia of Philosophy. Available at: http://plato.stanford.edu/archives/fall2012/entries/computing-responsibility/

Research ethics:

  • Van den Hoonaard, W. C. (2003). Is Anonymity an Artifact in Ethnographic Research? Journal of Academic Ethics, 1(2), 141-151
  • Hoeyer, K. L. (2012). Size matters: the ethical, legal, and social issues surrounding large-scale genetic biobank initiatives. Norsk epidemiologi,21(2).
  • Zimmer, M. (2010). "But the data is already public": on the ethics of research in Facebook. Ethics and Information Technology, 12(4), 313-325.

Values and information technologies:

  • Friedman, B., & Nissenbaum, H. (1996). Bias in computer systems. ACM Transactions on Information Systems (TOIS)14(3), 330-347.
  • Flanagan, M., Howe, D. C., & Nissenbaum, H. (2008). Embodying values in technology: Theory and practice. Information technology and moral philosophy, 322-353.
  • Simon J (2013) Distributed epistemic responsibility in a hyperconnected era. Available at:http://ec.europa.eu/digital-agenda/sites/digital-agenda/files/Contribution_Judith_Simon.pdf

Framing questions for information technologies:

  • Gillespie, Tarleton. "The Relevance of Algorithms." In Media Technologies: Essays on Communication, Materiality, and Society, edited by Tarleton Gillespie, Pablo J. Boczkowski, and Kirsten A. Foot. The MIT Press, 2014. MIT Press Scholarship Online, 2014. doi: 10.7551/mitpress/9780262525374.003.0009.
  • Burrell, J. (2016). How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data & Society, 3(1).

Big data as research field:

  • Judith Gregory and Geoffrey C. Bowker, The Data Citizen in Dawn Nufus (ed), Quantified: Biosensing Technologies in Everyday Life, MIT Press, forthcoming.
  • David Beer and Roger Burrows (2013): “Popular Culture, Digital Archives and the New Social Life of Data” Theory, Culture & Society 30:47.
  • Louise Amoore , Volha Piotukh  (2015): Life beyond big data: governing with little analytics Economy and Society 44 (3).

Big Data challenges:

Preparatory video materials: