Module SOCM033 for 2019/0
- Overview
- Aims and Learning Outcomes
- Module Content
- Indicative Reading List
- Assessment
Postgraduate Module Descriptor
SOCM033: Data Governance and Ethics
This module descriptor refers to the 2019/0 academic year.
How this Module is Assessed
In the tables below, you will see reference to 'ILO's. An ILO is an Intended Learning Outcome - see Aims and Learning Outcomes for details of the ILOs for this module.
Formative Assessment
A formative assessment is designed to give you feedback on your understanding of the module content but it will not count towards your mark for the module.
| Form of assessment | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|
| Presentation on essay topic | 10 minutes | 1-10 | Oral and written comments. |
Summative Assessment
A summative assessment counts towards your mark for the module. The table below tells you what percentage of your mark will come from which type of assessment.
| Coursework | Written exams | Practical exams |
|---|---|---|
| 100 | 0 | 0 |
...and this table provides further details on the summative assessments for this module.
| Form of assessment | % of credit | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|---|
| Written essay | 100 | 4000 words | 1-10 | Written comments. |
Re-assessment
Re-assessment takes place when the summative assessment has not been completed by the original deadline, and the student has been allowed to refer or defer it to a later date (this only happens following certain criteria and is always subject to exam board approval). For obvious reasons, re-assessments cannot be the same as the original assessment and so these alternatives are set. In cases where the form of assessment is the same, the content will nevertheless be different.
| Original form of assessment | Form of re-assessment | ILOs re-assessed | Timescale for re-assessment |
|---|---|---|---|
| Written essay | Written essay (4000 words) | 1-10 | August/September reassessment period |
Indicative Reading List
This reading list is indicative - i.e. it provides an idea of texts that may be useful to you on this module, but it is not considered to be a confirmed or compulsory reading list for this module.
Chris Anderson, “The end of theory: The data deluge makes the scientific method obsolete,” Wired, 23 June 2008, http://archive.wired.com/science/discoveries/magazine/16-07/pb_theory
David Beer, Metric Power 2016.
Paul N. Edwards, A vast machine: Computer models, climate data, and the politics of global warming (Cambridge, MA: MIT Press, 2010).
Paul N. Edwards, Matthew S. Mayernik, Archer L. Batcheller, Geoffrey C. Bowker, and Christine L. Borgman, “Science friction: Data, metadata, and collaboration,” Social studies of science 41 (2011): 667-690. http://dx.doi.org/10.1177/0306312711413314
Ford, Martin. 2018. Architects of Intelligence: The Truth about AI and the People Building It.
Julia Lane, Victoria Stodden, Stefan Bender, and Helen Nissenbaum, ed., Privacy, big data, and the public good: Frameworks for engagement. Cambridge: Cambridge University Press, 2014
Mittlestadt, B.D. and Floridi, L. (eds.) 2016. The Ethics of Biomedical Big Data. Springer.
Joanne Yates, Structuring the information age: Life insurance and technology in the twentieth century (Baltimore: Johns Hopkins University Press, 2008).
Leonelli, S. (2017) Biomedical Knowledge Production in the Age of Big Data. Report for the Swiss Science and Innovation Council, published online November 2017: http://www.swir.ch/images/stories/pdf/en/Exploratory_study_2_2017_Big_Data_SSIC_EN.pdf
Science International (2015). Big Data in an Open Data World. https://www.icsu.org/publications/open-data-in-a-big-data-world
Vayena, Effy, and John Tasioulas. 2016. “The Dynamics of Big Data and Human Rights: The Case of Scientific Research.” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 374 (2083): 20160129. doi:10.1098/rsta.2016.0129.
Zook, Matthew, Solon Barocas, danah boyd, Kate Crawford, Emily Keller, Seeta Peña Gangadharan, Alyssa Goodman, et al. 2017. “Ten Simple Rules for Responsible Big Data Research.” PLOS Computational Biology 13 (3): e1005399. doi:10.1371/journal.pcbi.1005399.
Borgman, Christine L. 2015. Big Data, Little Data, No Data. Cambridge, MA: MIT Press
Leonelli, S. (2016) Data-centric Biology: A Philosophical Study. Chicago University Press.
Gitelman, L. 2013. “Raw data” is an Oximoron. Cambridge: MIT Press.
Hey, T., Tansley, S., & Tolle, K. 2009. The fourth paradigm: Data-intensive scientific discovery. Redmond, WA: Microsoft Research.
Marr, B. 2015. Big Data: Using smart big data, analytics and metrics to take better decisions and improve performance. John Wiley & Sons.
Mayer-Schönberger, V., & Cukier, K. 2013. Big data: A revolution that will transform how we live, work, and think. New York: Eamon Dolan/Houghton Mifflin Harcourt.
Floridi L. 2014 The fourth revolution: how the infosphere is reshaping human reality. Oxford, UK:
Eubanks, Virginia. 2018. Automating Inequality: How High-Tech Tools Profile, Police and Punish the Poor.
O’Neill, C. 2016. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy.
Julia Lane, Victoria Stodden, Stefan Bender, and Helen Nissenbaum, ed., Privacy, big data, and the public good: Frameworks for engagement. Cambridge: Cambridge University Press, 2014
Ebeling, Mary F.E. 2016. Healthcare and Big Data: Digital Specters and Phantom Objects.
Leonelli, S. (2016) Locating Ethics in Data Science: Responsibility and Accountability in Global and Distributed Knowledge Production. Philosophical Transactions of the Royal Society: Part A.374: 20160122. http://dx.doi.org/10.1098/rsta.2016.0122
Levin, N. and Leonelli, S. (2016) How Does One “Open” Science? Questions of Value in Biological Research. Science, Technology and Human Values 42 (2): 280-305. DOI: 10.1177/0162243916672071
Viktor Mayer-Schönberger and Kenneth Cukier, Big data (New York: Houghton-Mifflin, 2013).
Leonelli, S. (2014) What Difference Does Quantity Make? On the Epistemology of Big Data in Biology. Big Data and Society 1: 1-11. http://bds.sagepub.com/content/spbds/1/1/2053951714534395.full.pdf
O’Neill and Shutt. 2017. Doing Data Science.
Harris, A., Kelly, S., Wyatt, S., 2016. CyberGenetics. Routledge, London.
Gina Neff, Venture labor: Work and the burden of risk in innovative industries (Cambridge, MA: MIT Press, 2012)
Srnicek, Nick. 2016. Platform Capitalism.
Thrift, Nigel. 2014. Knowing Capitalism. SAGE.
Zuboff, S. 2017. The Age of Surveillance Capitalism: The Fight for the Future at the New Frontier of Power.
