ST313      Half Unit
Ethics for Data Science

This information is for the 2025/26 session.

Course Convenor

Availability

Requisites

Pre-requisites:

Before taking this course, students must have completed: ST102 and MA100 and (MA222 or ST206 or ST202)

Additional requisites:

Students who have no previous experience in R are strongly recommended to complete an online pre-sessional R course from the Digital Skills Lab before the start of the course. (https://moodle.lse.ac.uk/course/view.php?id=8714).

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.

10 hours of lectures and 20 hours of classes in the Autumn Term.

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/

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.