Data Governance and Ethics - 2019 entry

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? This module (1) identifies 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 and the institutional commodification of personal data); (2) examines whether and how such concerns can be handled; and (3) discusses the responsibilities of data scientists and other producers of technologies for data analysis towards their proper use.

Pre-requisites: None

This module aims to equip students with the knowledge and skills to reason around the complex issues of data governance and ethics, and make good decisions in their 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.

Activities will include data analysis, data-supported decision-making, workshops and discussion of ethical/legal issues. 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 successful completion of this module, you should be able to:

Module Specific Skills and Knowledge:

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 Appreciate 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 and Knowledge:

5 Appreciate the wider social context 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 understanding of key contributions to scholarship on data studies and the digital society;

Personal and Key Transferable/ Employment Skills and Knowledge:

6 Effectively communicate complex ideas using written and verbal methods appropriate to the intended audience;

7 Demonstrate cognitive skills of critical and reflective thinking;

8 Demonstrate effective independent study and research skills.

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;

• 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 the Met Office;

• Cinema event and discussion.

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 50%) 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 50%.

Basic Reading:

ELE: http://vle.exeter.ac.uk/

Web based and Electronic Resources:

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

E. L. Lehmann, Reminiscences of a statistician: The Company I Kept (Springer, 2008):
http://dx.doi.org/10.1007/978-0-387-71597-1

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

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

Leonelli, S. (2014) What Difference Does Quantity Make? On the Epistemology of Big Data in Biology. Big Data and Society 1: 1-11:
https://journals.sagepub.com/doi/10.1177/2053951714534395

Other Resources:

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

Hilgartner, S., 2013. Constituting large-scale biology: Building a regime of governance in the early years of the Human Genome Project. BioSocieties 8, 397–416

Reading list for this module: