Data and Society

Summary

The purpose of the course is for the student to develop an understanding of the societal consequences (risks and favourable opportunities) of today's increasing digitalisation, and to be able to convey knowledge about methods and tools in the field of data science, with significance for decision making and societal development.

Admission requirements

  1. Bachelor of Science in computer science or related subjects.
  2. At least 15 credits in programming.
  3. At least 7.5 credits in mathematics.
  4. Knowledge equivalent to English 6 at Swedish upper secondary level

Selection:

credits 100%

Syllabus

Syllabus for students autumn 2020

Course Code:
DA630E revision 1
Swedish name:
Samhällsaspekter av databehandling
Level of specialisation
A1N
Main fields of study:
Computer Science
Language:
English
Date of ratification:
25 March 2019
Decision-making body:
Faculty of Technology and Society
Enforcement date:
31 August 2020

Entry requirements

  1. Bachelor of Science in computer science or related subjects.
  2. At least 15 credits in programming.
  3. At least 7.5 credits in mathematics.
  4. Knowledge equivalent to English 6 at Swedish upper secondary level

Specialisation and progression relative to the degree regulations

The course is part of the programme Computer Science: Applied Data Science, master’s programme, and can be included in the master's degree in computer science (120 credits).

Purpose

The purpose of the course is for the student to develop an understanding of the societal consequences (risks and favourable opportunities) of today's increasing digitalisation, and to be able to convey knowledge about methods and tools in the field of data science, with significance for decision making and societal development.

Contents

The course contains the following elements:

The course focuses on how modern information technology and data processing gives rise to new kinds of ethical dilemmas. The course uses case studies to convey principles and guidelines for managing:

  • Data quality
  • Data integrity and responsible management of sensitive data (e.g. GDPR, consent)
  • Data security (including data ownership, block chain techniques, threats and risk analysis)
  • Data-driven and evidence-based decision making
  • Neutrality, transparency, reliability, accountability and personal self-determination in a data-driven society.
  • Interpretability in machine learning models
  • Susceptibility to bias and discrimination as a potential effect of information systems replacing manual processing
  • Ethical perspective on data science

Learning outcomes

Knowledge and understanding
For a passing grade the student shall be able to:

  • Summarise current reasoning on ethical aspects of data-driven activities
  • Describe conditions for the results of data processing being interpreted correctly
  • Discuss ethical aspects of data processing and their significance for decision-making and societal development
Competence and abilities
For a passing grade the student shall be able to:
  • Identify pitfalls and weaknesses in today's data-driven systems and provide solutions to mitigate them.
  • Point out possible contributions from the use of data science to policymakers and stakeholders in the public and private sector
  • Verbally presenting a work in the field of data science
Evaluation abilities and approach
For a passing grade the student shall be able to:
  • Analyse ethical problems concerning data processing and argue from an ethical perspective to suggest improvements to data-driven systems and operations.
  • Critically analyse and justify ethical positions in relation to data science processes

Learning activities

Lectures and seminars.

Assessments

The students’ performance is assessed partly from written assignments (6.5 credits, assessed as A–E), and partly from oral presentations (1 credit, assessed as UG)
An A-E pass requires that all parts have been completed and passed. The final grade is based on written assignments.

Grading system

Excellent (A), Very Good (B), Good (C), Satisfactory (D), Pass (E) or Fail (U).

Course literature and other teaching materials

  • Tavani, Herman T. (2013) Ethics and Technology Controversies, Questions and Strategies for Ethical Computing, Fourth Edition. John Wiley & Sons Inc.
A collection of scientific articles will be added to the above mentioned literature.

Course evaluation

The University provides students who are taking or have completed a course with the opportunity to share their experiences of and opinions about the course in the form of a course evaluation that is arranged by the University. The University compiles the course evaluations and notifies the results and any decisions regarding actions brought about by the course evaluations. The results shall be kept available for the students. (HF 1:14).

Interim rules

When a course is no longer given, or the contents have been radically changed, the student has the right to re-take the examination, which will be given twice during a one year period, according to the syllabus which was valid at the time of registration.

Other Information

The syllabus is a translation of a Swedish source text.

Contact

The education is provided by the Faculty of Technology and Society at the Department of Computer Science and Media Technology.

Further information

Application

09 November 2020 - 17 January 2021 Day-time 50% Malmö This course is offered as part of a program

Tuition fees

for non-EU students only

First instalment: 15000 SEK
Full tuition Fee: 15000 SEK

09 November 2020 - 17 January 2021 Day-time 50% Malmö

Tuition fees

for non-EU students only

First instalment: 15000 SEK
Full tuition Fee: 15000 SEK