Teaching plan for the course unit

 

 

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General information

 

Course unit name: Ethics for Data Science

Course unit code: 574185

Academic year: 2021-2022

Coordinator: Jordi Vitria Marca

Department: Faculty of Mathematics and Computer Science

Credits: 3

Single program: S

 

 

Estimated learning time

Total number of hours 75

 

Face-to-face and/or online activities

30

 

-  Lecture with practical component

Face-to-face

 

30

Supervised project

15

Independent learning

30

 

 

Competences to be gained during study

 

  • Knowledge of legislation on data protection and privacy, and on the ethical code in professional practice.
  • Capacity to communicate results using appropriate communication and display techniques.
  • Capacity to verify and quantify the validity of a hypothesis, using data analysis.

 

 

 

 

Learning objectives

 

Referring to knowledge

Data science has the potential to be both beneficial and detrimental to individuals and/or the wider public. To help minimize any adverse effects, we must seek to understand the potential impact of our work and consider any opportunities that may deliver benefits for the public. It is recognized that not all work will have a defined societal benefit, but we could strive to seek fairness or an overall increase to well being, within commercial applications.

In this course, we will explore the moral, social, and ethical ramifications of the choices we make at the different stages of the data analysis pipeline, from data collection and storage to understand feedback loops in the analysis. Through class discussions, case studies, and exercises, students will learn the basics of ethical thinking, understand some tools to check or mitigate undesired effects, and study the distinct challenges associated with ethics in modern data science.

 

 

Teaching blocks

 

1. A quick tour through the foundations of ethics.

2. Privacy. Tools for preserving privacy.

3. Transparency, Explainability and Causality.

4. Data Science and Society.

5. Fairness.

 

 

Teaching methods and general organization

 

  • Weekly, there is a face-to-face two-hour session corresponding to theoretical-practical activities.


 

 

 

Official assessment of learning outcomes

 

Problem Sets and exercises: 100%. 

 

Examination-based assessment

Problem Sets and exercises: 100%.