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Data Governance and Ethics

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 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, and use (and mis-use) of social data will be explored through lectures and seminars.

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

Learning activities and teaching methods (given in hours of study time)

Details of learning activities and teaching methods

Formative assessment

Summative assessment (% of credit)

Details of summative assessment

Details of re-assessment (where required by referral or deferral)

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
• Birhane, A., 2021. Algorithmic injustice: a relational ethics approach. Patterns, 2(2).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
• 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
• 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.
• 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:
• Floridi, L. and Cowls, J., 2019. A Unified Framework of Five Principles for AI in Society, Harvard Data Science Review, 1(1). doi:10.1162/99608f92.8cd550d1.
• 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
• Beaulieu, A. and Leonelli, S., 2021. Data and Society: A Critical Introduction. SAGE Publications.
• 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.

• Srnicek, Nick. 2016. Platform Capitalism.
• Zuboff, S. 2017. The Age of Surveillance Capitalism: The Fight for the Future at the New Frontier of Power.

Indicative learning resources - Web based and electronic resources

ELE – vle.exeter.ac.uk/

Key words search

Data science; ethics; data governance; AI: AI ethics