Postgraduate Module Descriptor

PHLM011: Data Governance and Ethics

This module descriptor refers to the 2019/0 academic year.

Module Aims

This module aims to equip you with the knowledge and skills to reason around the complex issues of data governance and ethics, and make good decisions in your own professional and personal practice of data management. The module introduces the key ethical questions around the use of big data and associated technologies such as machine learning and artificial intelligence, and places them in the broader framework of contemporary digital society (including its reliance on automation, social media and related platforms for communication and service provision). The legal and social contexts for decision-making will be explored through a number of real-world case studies. Each case study will be examined from end to end, beginning with a real-world example of data collection, storage and analysis, following the possible (intended and unintended) ways in data is subsequently used to support decision-making, and considering the ethical and legal issues that arise at each stage. Key issues such as data protection, open data, citizen science and use (and mis-use) of social data will be explored through lectures and seminars.

Assessment will be based on an essay considering a chosen aspect of data governance and ethics. Guest lectures by practitioners responsible for data governance in different contexts will enrich the course content. 

Intended Learning Outcomes (ILOs)

This module's assessment will evaluate your achievement of the ILOs listed here - you will see reference to these ILO numbers in the details of the assessment for this module.
On successfully completing the programme you will be able to:
Module-Specific Skills1. Evaluate the choices made at each stage of a data science process and the associated legal, ethical and governance issues.
2. Identify key social concerns in relation to digital tools within contemporary society.
3. Understand the core regulatory and legislative frameworks that govern collection, storage, processing and communication of data.
4. Assess and critically evaluate the differing costs and benefits associated with use of data when considered from perspectives of data user, data provider, decision-maker and regulator.
Discipline-Specific Skills5. Evaluate the social contexts of data science and related technologies, including current issues such as open data, data protection, automated data analysis, and misuse of data and related analytics.
6. Critically reflect on the ethical considerations associated with use of data within organisations and governments.
7. Display a comprehensive and critical understanding of key contributions to scholarship on data studies and the digital society.
Personal and Key Skills8. Effectively communicate complex ideas using written and verbal methods appropriate to the intended audience.
9. Demonstrate cognitive skills of critical and reflective thinking.
10. Demonstrate effective independent study and research skills.

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.