Module PHLM011 for 2019/0
- Overview
- Aims and Learning Outcomes
- Module Content
- Indicative Reading List
- Assessment
Postgraduate Module Descriptor
PHLM011: Data Governance and Ethics
This module descriptor refers to the 2019/0 academic year.
Module Content
Syllabus Plan
Topics will include:
- Measuring society? Data governance and ethics.
- How to deal with exclusions: fairness in data collection and analysis.
- Social justice and the politics of evidence-based movements.
- The advantages and disadvantages of automation.
- The professional status of data scientists and their role relating to government, research institutions, industry and societal expectations.
- Historical roots and current institutionalisation of data science.
- Data science across fields: the challenges of diversity.
- Case Study 1: Scraping data from Twitter and other social media. Issues of privacy, sample bias and fairness.
- Case Study 2: Personalised medicine. Maintaining trust: identifying and handling ethical concerns in data science.
- Case Study 3: Engagement and participation: the opportunities of citizen science. With guest lecture from Met Office.
- Cinema event & discussion.
Learning and Teaching
This table provides an overview of how your hours of study for this module are allocated:
| Scheduled Learning and Teaching Activities | Guided independent study | Placement / study abroad |
|---|---|---|
| 22 | 128 | 0 |
...and this table provides a more detailed breakdown of the hours allocated to various study activities:
| Category | Hours of study time | Description |
|---|---|---|
| Scheduled Learning and Teaching | 22 | Lectures and discussion (two hours per week) |
| Guided Independent Study | 78 | Background reading |
| Guided Independent Study | 50 | Coursework preparation and writing. |
Online Resources
This module has online resources available via ELE (the Exeter Learning Environment).
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.
