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 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.
| On successfully completing the programme you will be able to: | |
|---|---|
| Module-Specific Skills | 1. 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 Skills | 5. 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 Skills | 8. 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. |
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).
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.
