Data Governance and Ethics
| Module title | Data Governance and Ethics |
|---|---|
| Module code | SOCM033 |
| Academic year | 2024/5 |
| Credits | 15 |
| Module staff | Dr Stephan Guttinger (Convenor) Dr Silvia Milano (Convenor) |
| Duration: Term | 1 | 2 | 3 |
|---|---|---|---|
| Duration: Weeks | 11 |
| Number students taking module (anticipated) | 240 |
|---|
Module description
Data science, machine learning, artificial intelligence and “big data” have become central to every aspect of social life. How can these complex and powerful technologies best be managed and governed for the benefit of society now and in the future? In this module you will: (1) identify some of the main risks and ethical/legal challenges involved in the widespread automation and digitalisation of services characterising 21st century life (for example, the clash between individual desire for privacy, frameworks for data ownership and the institutional commodification of personal data); (2) examine whether and how such concerns can be handled; and (3) discuss the responsibilities of data scientists and other producers of technologies for data analysis towards their proper use.
Module aims - intentions of the module
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.
Guest lectures by practitioners responsible for data governance in different contexts will enrich the course content.
Intended Learning Outcomes (ILOs)
ILO: Module-specific skills
On successfully completing the module you will be able to...
- 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.
ILO: Discipline-specific skills
On successfully completing the module you will be able to...
- 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.
ILO: Personal and key skills
On successfully completing the module you will be able to...
- 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.
Syllabus plan
Whilst the precise content may vary from year to year, it is envisaged that the syllabus will cover all or some of the following topics:
- 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.
Learning activities and teaching methods (given in hours of study time)
| Scheduled Learning and Teaching Activities | Guided independent study | Placement / study abroad |
|---|---|---|
| 22 | 128 | 0 |
Details of learning activities and teaching methods
| Category | Hours of study time | Description |
|---|---|---|
| Scheduled Learning and Teaching | 22 | 11 x 2 hour lectures and discussion (two hours per week) |
| Guided Independent Study | 78 | Background reading |
| Guided Independent Study | 50 | Coursework preparation and writing |
Formative assessment
| Form of assessment | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|
| Presentation on essay topic | 7 minutes | 1-10 | Oral and written comments. |
Summative assessment (% of credit)
| Coursework | Written exams | Practical exams |
|---|---|---|
| 100 | 0 | 0 |
Details of summative assessment
| Form of assessment | % of credit | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|---|
| Written essay | 100 | 3000 words | 1-10 | Written comments |
| 0 | ||||
| 0 | ||||
| 0 | ||||
| 0 | ||||
| 0 |
Details of re-assessment (where required by referral or deferral)
| Original form of assessment | Form of re-assessment | ILOs re-assessed | Timescale for re-assessment |
|---|---|---|---|
| Written essay | Written essay (3000 words) | 1-10 | August/September reassessment period |
Indicative learning resources - Basic reading
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.
| Credit value | 15 |
|---|---|
| Module ECTS | 7.5 |
| Module pre-requisites | None |
| Module co-requisites | None |
| NQF level (module) | 7 |
| Available as distance learning? | No |
| Origin date | 30/11/2018 |
| Last revision date | 06/05/2022 |
