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Department of Computer Science and Technology

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MPhil ACS, Part III
Michaelmas term
Course code: 
Class limit: 


Data-driven technologies are increasingly the subject of social commentary, political scrutiny and regulatory attention. This module aims to develop a solid understanding of the practical implications these concerns have on systems design and engineering.

Areas explored include the legal foundations in data protection (GDPR), privacy, liability, human rights; issues of tech-surveillance; algorithmic fairness, accountability and transparency (the so-called 'FAT'); and the related implications for technologies including cloud, machine learning and the IoT.

This course provides students with a practical background regarding how law, policy and societal concerns interact with technology. This is to develop an awareness and consideration of how systems can be designed and engineered to be more accountable, legally compliant, and generally better for society.

Syllabus and coursework

The course will consist of eight seminars covering the following topics:

  1. Introduction and foundations of tech-law
  2. Privacy, data protection (GDPR), tech-surveillance
  3. Clouds, platforms and app ecosystems
  4. Internet of Things
  5. Algorithmic accountability (ML / automated decision making)

Seminars will be interactive and discussion-oriented, with foundational materials presented in a lecture-like format. Students will be allocated one or more reading materials to present to the group. Coursework will also include short comment pieces, and a research essay on relevant topics.


On completion of this module, students should:

  • Appreciate the practical considerations and challenges of engineering more compliant and accountable systems;
  • Understand the key legal, regulatory and social influences on technical design;
  • Be familiar with the interdisciplinary tech-legal research landscapes;
  • Appreciate the ongoing legal, policy and societal debates concerning emerging technology.


  • Presentation(s) of reading material (30%);
  • Short comment pieces on topical issues (max 500 words) (20%);
  • A research essay (max 2500 words). Short presentations in the final week (40%);
  • General participation (10%).

Recommended Reading

Specific reading materials will be set according to each week’s topic.

For some general background and context, suggested reading includes:

Arbesman, Samuel. (2017) Overcomplicated, Portfolio.

Eubaks, Virginia. (2019) Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor, Picador USA.

Lessig, Lawrence (2006) Code, and other laws of cyberspace, Basic Books

Hildebrandt, Mireille. (2020) Law for Computer Scientists, Oxford

Schneier, Bruce (2012) Liars and Outliers, John Wiley & Sons

O'Neil, Cathy. (2017) Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, Penguin.

Pasquale, Frank. (2016) The Black Box Society: The Secret Algorithms That Control Money and Information. Harvard.

Principles for Accountable Algorithms

Handbook on European Data Protection Law 2018

Further Information

Due to COVID-19, the method of teaching for this module will be adjusted to cater for physical distancing and students who are working remotely. We will confirm precisely how the module will be taught closer to the start of term.