Data Science in Society - 2019 entry

This module will focus on the societal context for data science, machine learning and artificial intelligence. An increasing number of social, governmental and commercial processes now take place in online or digital environments, making it important to consider the ways in which data is used to make decisions and how the application of computational methods to engineer social processes can be managed in ways that are ethical, transparent and socially acceptable. This module will teach you the core knowledge around data ethics, privacy, fairness and data governance. You will be encouraged to form your own opinions on how digital tools can best be developed to deliver benefits and avoid harm. Seminar discussions and ethical case studies will be used to highlight and explore different social issues around data science.

Pre-requisites: None

Co-requisites: None

Suitable for non-specialists and interdisciplinary pathways

This module aims to:

• Explore how data science and analytics can utilised in ways that are responsive to broader social concerns in the UK and internationally;

• Discuss data ethics and societal issues around data collection, processing, dissemination and use in decision-making;

• Enhance knowledge of data protection policies at local, national and international level;

• Provide skills in engaging data providers, users and customers to ensure compliance with regulations and legal systems;

• Ensure awareness of social context of data science with attention to potential ethical concerns including privacy, fairness and bias;

• Develop skills in assessing data science services (and particularly Big Data) for the general public as well as specific stakeholders (government, local authorities, competitors in industry, lobby groups).

The module will draw on recent scholarship in data studies and case studies based on a variety of contexts. The module provides training in ethical and societal implications of data collection, processing and management strategies.

On successful completion of this module, you should be able to:

Module Specific Skills and Knowledge:

1 Understand key terms and concepts in data ethics, data governance and information management and be able to relate these to a typical business situation;

2 Understand how to manage data collection, processing, storage and dissemination within current legal frameworks;

3 Understand some ethical issues relevant to data science and how ethical concerns can be managed;

Discipline Specific Skills and Knowledge:

4 Apply theoretical arguments, frameworks and concepts from data studies to the use of data science in an organisational context;

5 Demonstrate an integrated and holistic perspective when generating data science solutions;

6 Develop knowledge of professional responsibility and ethics around data science;

Personal and Key Transferable/ Employment Skills and Knowledge:

7 Demonstrate cognitive skills of critical and reflective thinking;

8 Demonstrate effective critical reading and discussion skills.

The module will cover:

• The role of data science in society;

• Basic concepts of data ethics and data governance;

• Applying current concepts in data ethics and data governance to real examples;

• Understanding the legal frameworks around use and analysis of data in social and business contexts;

• Intellectual Property, legal regimes and regulatory structures around the ownership and maintenance of databases;

• Data dissemination, curation and Open Data, the limits of automation, and the challenges of making data accessible and re-usable;

• Data protection and legal frameworks for data collection, storage and analysis.

Deferral – if you miss an assessment for certificated reasons judged acceptable by the Mitigation Committee, you will normally be either deferred in the assessment or an extension may be granted. The mark given for a re-assessment taken as a result of deferral will not be capped and will be treated as it would be if it were your first attempt at the assessment.

Referral – if you have failed the module overall (i.e. a final overall module mark of less than 40%) you will be required to re-take some, or all, parts of the assessment, as decided by the Module Convenor. The final mark given for a module where re-assessment was taken as a result of referral will be capped at 40%.

Basic Reading:

ELE: http://vle.exeter.ac.uk/

Schutt, R. and O’Neill, C., Doing Data Science: Straight Talk from the Frontline.
Boyd, D. and Crawford, K., Six provocations for Big Data. A Decade in Internet Time: Symposium on the Dynamics of the Internet and Society.
Kitchin, R., The Data Revolution.
Leonelli, S., Data-Centric Biology: A Philosophical Study.

Other Resources:

Science International (2015). Big Data in an Open Data World.

Hey, Tony, et al. 2009. The Fourth Paradigm: Data-Intensive Scientific Discovery, Microsoft Publishing.

Hine, Christine. 2006. ‘‘Databases as Scientific Instruments and Their Role in the Ordering of Scientific Work.’’ Social Studies of Science 36 (2): 269-98.

Dove, Edward S., Yann Joly, Anne-Marie Tassé, Paul Burton, Rex Chisholm, Isabel Fortier, Pat Goodwin, et al. 2015. “Genomic Cloud Computing: Legal and Ethical Points to Consider.” European Journal of Human Genetics 23 (10): 1271–78. doi:10.1038/ejhg.2014.196.

Dove, Edward S., David Townend, Eric M. Meslin, Martin Bobrow, Katherine Littler, Dianne Nicol, Jantina de Vries, et al. 2016. “Ethics Review for nternational Data-Intensive Research.” Science 351 (6280): 1399–1400. doi:10.1126/science.aad5269.

Burton, Paul R., Madeleine J. Murtagh, Andy Boyd, James B. Williams, Edward S. Dove, Susan E. Wallace, Anne-Marie Tassé, et al. 2015. “Data Safe Havens in Health Research and Healthcare.” Bioinformatics 31 (20): 3241–48. doi:10.1093/bioinformatics/btv279.

Boulton, Geoffrey, Brian Campbell, Brian Collins, Peter Elias, Wendy Hall, Graeme Laurie, Onora O’Neill, et al. 2012. “Science as an Open Enterprise.” 02/12. London: The Royal Society Science Policy Centre.

Reading list for this module: