ST313 Half Unit
Ethics for Data Science
This information is for the 2025/26 session.
Course Convenor
Christine Yuen
Kaifang Zhou
Availability
This course is available on the BSc in Actuarial Science, BSc in Actuarial Science (with a Placement Year), BSc in Data Science, BSc in Mathematics and Economics, BSc in Mathematics with Data Science, BSc in Mathematics with Economics, BSc in Mathematics, Statistics and Business, Erasmus Reciprocal Programme of Study and Exchange Programme for Students from University of California, Berkeley. This course is available with permission as an outside option to students on other programmes where regulations permit. This course is available with permission to General Course students.
Requisites
Pre-requisites:
Before taking this course, students must have completed: ST102 and MA100 and (MA222 or ST206 or ST202)
Course content
This course covers a selection of topics central to the ethical practice of data science. Students will learn key concepts and methods to analyze a variety of case studies, from the historical and philosophical background of data technologies and ethics to the frontiers of research in machine learning, artificial intelligence, and socio-technical systems. These concepts will include some basic philosophical and legal ideas related to data ethics, frameworks for ethical practice developed by professional societies, formal statistical definitions and quantitative methods for objectives such as fairness and privacy, and an emphasis on the use of causal reasoning to evaluate data-driven systems and policies. Topics may include:
- Replication crisis, unfair algorithms, basics of normative ethics and causality
- Historical examples, professional ethical guidelines
- Transparency, reproducibility, open science
- Discrimination, statistical fairness, impossibility results
- Causal reasoning for fairness, pathway analysis, intersectionality
- Interventions, policy optimization, distributive justice
- Data provenance, privacy, differential privacy
- Strategic behavior, surveillance, democratic data
- Automation and AI, responsibility, complicity
Causal statistical models will be used as a formal framework throughout to understand and stress test these ideas.
Teaching
10 hours of lectures and 20 hours of classes in the Autumn Term.
Indicative reading
Lecture notes will be provided. These will be supplemented with a variety of short readings, some of which will be taken from the following background references
- https://www.bitbybitbook.com/en/1st-ed/ethics/
- https://fairmlbook.org/
- https://data-feminism.mitpress.mit.edu/
- https://aiethics.princeton.edu/case-studies/
- https://www.acm.org/code-of-ethics
- https://rss.org.uk/RSS/media/News-and-publications/Publications/Reports%20and%20guides/A-Guide-for-Ethical-Data-Science-Final-Oct-2019.pdf
- https://www.amstat.org/ASA/Your-Career/Ethical-Guidelines-for-Statistical-Practice.aspx
- https://hastie.su.domains/CASI/
- https://www.statlearning.com/
Assessment
Quiz (30%) in Autumn Term Week 11
Course participation (20%)
Project (50%)
Group work consists of a project proposal in AT, and the project itself will then be due in the WT.
Key facts
Department: Statistics
Course Study Period: Autumn Term
Unit value: Half unit
FHEQ Level: Level 6
CEFR Level: Null
Total students 2024/25: 51
Average class size 2024/25: 26
Capped 2024/25: NoCourse selection videos
Some departments have produced short videos to introduce their courses. Please refer to the course selection videos index page for further information.
Personal development skills
- Self-management
- Team working
- Problem solving
- Application of information skills
- Communication
- Application of numeracy skills
- Commercial awareness
- Specialist skills