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.

Intended Learning Outcomes (ILOs)
This module's assessment will evaluate your achievement of the ILOs listed here - you will see reference to these ILO numbers in the details of the assessment for this module.
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).