Aims

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 and algorithmic accountability; 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 support accountability, be more legally compliant, and generally better for society.

Syllabus and coursework

The course will cover the following topics:

1. Introduction and foundations of tech-law
2. Data protection (GDPR): practical implications
3. Privacy and techno-surveillance
4. Clouds, platforms and app ecosystems
5. Internet of Things
6. Algorithmic accountability [inc. automated decision making and machine learning]
7. Audit, compliance and oversight

Seminars will be discussion-oriented, with foundational material presented in a tutorial-like format. Students will be allocated one or more reading materials to present to the group. Coursework will also include short comment pieces, and either a research essay on relevant topics or a practical “accountability engineering” exercise (proposals to be discussed).

Objectives

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.

Assessment

• Presentation(s) of reading material (30%);
• Short comment pieces on topical issues (max 500 words) (20%);
• A research essay (max 2500 words) or accountability engineering exercise. 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.

Code, and other laws of cyberspace by Lawrence Lessig

Liars & Outliers by Bruce Schneier

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.

Handbook on European Data Protection Law 2018