Ethics and ICT

lfsa2202  2018-2019  Louvain-la-Neuve

Ethics and ICT
3 credits
30.0 h
Q2
Teacher(s)
Gosseries Axel; Pereira Olivier;
Language
English
Prerequisites
No specific prerequisites. An interest for ICT issues and a curiosity for their ethical/policy dimensions are perfectly sufficient.
Main themes
The themes change from year to year, and may include:
- net neutrality and freedom of expression
- intellectual property
- privacy
- crowdsourcing and the "sharing economy"
- cryptocurrencies
  • - e-democracy
Aims

At the end of this learning unit, the student is able to :

1

Contribution of the course to the program objectives (N°)

3.2, 3.3

4.2, 4.3

5.2, 5.5, 5.6

6.2, 6.3, 6.4

Specific learning outcomes of the course

a.      Disciplinary Learning Outcomes

Students having successfully followed this course will be :

  • understanding why ethical reasoning is relevant for their specific disciplinary domain and more generally ;
  • ble to identify within an ICT ethical/policy question which parts of the debate have to do with factual questions and which parts belong to a properly ethical dimension and how they should be articulated ;
  • able to formulate an ethical question when facing a technical choice with an ethical dimension, and dare stating and arguing for their own view on the question ;
  • grasping the spirit of a method in ethics to answer a given question, in relation to ICT ;
  • aware of the content of the central arguments and rationale on issues such as, for example, freedom of expression, intellectual property, privacy, e-democracy and how they lead to specific challenges in well defined ICT contexts.

b.     Transversal Learning Outcomes

 Students having successfully followed this course will be :

  • able to access the relevant sources on the topics seen in class and on other related ones ;
  • able to work both individually and in group
  • able to answer a question in a short well-structured written format, in non-technical language ;
  • able to present their work orally.
 

The contribution of this Teaching Unit to the development and command of the skills and learning outcomes of the programme(s) can be accessed at the end of this sheet, in the section entitled “Programmes/courses offering this Teaching Unit”.
Content
The content will be adapted depending on the themes investigated in the class.
Teaching methods
a.    Process organization
The course will contain a mix of:
- Lectures focusing on background and methodology,
- Sessions during which students will be requested to debate and elaborate a position on a specific issue, which they will present either in writing or orally.

b.    Media

Students will be provided for each specific theme with a short reading list. They will also be provided with a short general bibliography on the theme.
Evaluation methods
The students will be graded, based on the learning outcomes described above, from oral examination and from essays that they will submit individually or in small groups. This may take the form of a short draft essay benefiting from a detailed feedback from the professors and from an oral discussion with colleagues before being handed-in in final version.
This may also take the form of a written exam during the exam session and/or of a long writing session in class during term time.
Online resources
http://moodleucl.uclouvain.be/enrol/index.php?id=4833
Bibliography
Supports de cours, ouvrages de références, ...
Faculty or entity
EPL


Programmes / formations proposant cette unité d'enseignement (UE)

Title of the programme
Sigle
Credits
Prerequisites
Aims
Master [120] in Data Science Engineering

Master [120] in Electro-mechanical Engineering

Master [120] in Mechanical Engineering

Master [120] in Computer Science and Engineering


Master [120] in Civil Engineering

Master [120] in Electrical Engineering

Master [120] in Physical Engineering

Master [120] in Chemical and Materials Engineering

Master [120] in Political Sciences: General

Master [120] in Biomedical Engineering

Master [120] in Computer Science

Master [120] in Mathematical Engineering

Master [120] in data Science: Information technology