Students will learn how to:
1) translate abstract concepts in ethics into practical tools for reasoning and decisionmaking,
2) identify and distinguish between immediate, medium term and longer term ethical/legal problems,
3) predict and reason about the social, legal and environmental impacts of their work as data science entrepreneurs.
- Analytical writing;
- Evaluating each other’s work on different levels;
- Balancing the demands of different domains with regard to entrepreneurship;
- Making complex considerations accessible in the form of a briefing/proposal
Participation component of the grade is based on weeks 1-14 of the course. Written assignment component will be produced during weeks 12-14
This course builds on JM0160 – IP and Privacy
Required software and hardware: N/A
Clicker required: yes.
- Lectures, self-study and group work will be used equally throughout the course, and tutorials will take place in the final weeks as students work on the written assignment.
- The sessions will be structured as follows (except for week 1, which will consist of one introductory lecture):
o session 1 (Monday 10.30-12.30) debate
o session 2 (Monday 13.45-15.30) lecture
- Students will be expected to work in groups on one case each week, based on the lecture and readings provided, and to debate their findings in class. The final (individual) assignment will take the form of writing a blog post on a public forum, aiming to inform the entrepreneurship community about current issues in data ethics. Specialist guest lecturers will be drawn from the international technical, legal and business communities.
- Lecture: 2 hrs per week
JM0160 – Intellectual Property and Privacy
- O’Neil, C, Weapons of math destruction: How big data increases inequality and threatens democracy, New York: Crown Publishing Group., 2016.
Group work (presentation & debate): 2 hrs per week
Week 1 Introduction to the course
Week 2 Identity and identification
Week 3 Data access and legitimacy
Week 4 Data for policy
Week 5 Discrimination, bias and error
Week 6 Reading week
Week 7 IoT security and privacy
Week 8 Value-sensitive design
Week 9 Responsibility and accountability
Week 10 The cloud(s)
Week 11 Social privacy
Week 12 Experimentation
Week 13 - 14 Discussion and tutorial for final assignment
- Required literature will be composed of academic and policy papers relating to particular weeks’ topics; specific readings will be indicated in the syllabus.