Ethics and Data Governance - PSY00121M

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Module summary

Regardless of the type of data you are working with, managing those data in a secure and transparent manner is a key part of data science. This module aims to discuss the key considerations that form part of data governance and ethics.

Module will run

Occurrence	Teaching period
A	Semester 1 2025-26

Module aims

Regardless of the type of data you are working with, managing those data in a secure and transparent manner is a key part of data science. This module aims to discuss the key considerations that form part of data governance. This includes thinking carefully about the way you want to collect, access, store, use, and analyse data while ensuring quality, integrity, and security. In the module we will walk through various stages of the data life cycle. You will learn how to write a data management plan and we will discuss ethical considerations, in particular when working with personal data. We will also discuss ethical and GDPR issues related to use of data on publicly available platforms, including social media.

You will also learn about various open research practices that are used across disciplines to make research as open and transparent as possible. You will gain hands-on experience applying and critically evaluating several of these practices, including pre-registration, data sharing, and working with metadata. Finally, we will discuss AI (Artificial Intelligence) and the consequences AI developments can have for the area of data science.

Module learning outcomes

• Discuss the stages of the data life cycle for different types of data
• Critically evaluate the importance of and risks associated with data access, security, analysis, and retention
• Design and evaluate data management plans
• Evaluate ethics implications and data protection considerations when working with personal data
• Formulate data collection and analysis plans in a pre-registration format
• Critically evaluate best practices when using metadata
• Discuss best practices when sharing data
• Debate the implications of AI for data science
• Examine risks associated with malpractices in data science
• Assess different approaches towards data governance

Module content

• Components of the data life cycle, when working with primary and secondary data
• Data management plans (including data access, security, retention, and consequences of data breach)
• Working with personal and sensitive data: Ethical and data protection considerations
• Pre-registration of data collection/access and analysis plans
• Writing and evaluating metadata
• Data sharing practices and considerations
• Influence of AI on research and data science

A large part of the module will discuss these topics by critically evaluating different examples of data governance practices. Lectures will therefore include more practical components where you work in groups on the evaluation and development of the various components of the data life cycle.

Indicative assessment

Task	% of module mark
Essay/coursework	100

Special assessment rules

None

Indicative reassessment

Task	% of module mark
Essay/coursework	100

Module feedback

Marks will be available on e:vision.

Indicative reading

Michener, W. K. (2015). Ten simple rules for creating a good data management plan. PLoS computational biology, 11(10), e1004525.

Kathawalla, U. K., Silverstein, P., & Syed, M. (2021). Easing into open science: A guide for graduate students and their advisors. Collabra: Psychology, 7(1), 18684.

Norori, N., Hu, Q., Aellen, F. M., Faraci, F. D., & Tzovara, A. (2021). Addressing bias in big data and AI for health care: A call for open science. Patterns, 2(10).

Wang, D., Weisz, J. D., Muller, M., Ram, P., Geyer, W., Dugan, C., ... & Gray, A. (2019). Human-AI collaboration in data science: Exploring data scientists' perceptions of automated AI. Proceedings of the ACM on human-computer interaction, 3(CSCW), 1-24.