Responsible Data Science
Summary 2021
David Schouten
0. Course Introduction 2
1. Introduction to Responsible Data Science 5
2. The Data Dimension 9
3. The Algorithm Dimension 14
4. The Human Dimension 16
5. The Design Dimension Data visualisation 20
6. The Design Dimension: Explainable AI 24
6. The Design Dimension: Explainable AI Part 2 27
Exam information 31
1
,0. Course Introduction
26 April 2021
The course is taught by Evanthia Dimara, Jens Gulden and Anna Wegmann
The course is about a very multidisciplinary topic: designs aspects, engineering
aspects, psychological aspects, etc. So the best way to cover it is to circle around it
and cover all facets. It does this by taking on a high level scope, with less focus on
“technical” data science.
Data science is about accuracy and efficiency: what can we do with data.
Responsible data science is about what we should and shouldn’t do with data.
Deliverables and deadlines:
2
,The workshops are meant for prototyping, literature review, algorithms, writing and
critical thinking. They are mainly led by Anna and she also manages the Teams
channels.
Everything regarding the course is posted on MS Teams: Lectures, communication,
class materials, announcements, grades. Lectures are not recorded because a lot of
critical topics are discussed during the lectures: racism, discrimination, ethics. These
can bring a lot of discussions and everyone should feel confident to share their views
and thoughts. Blackboard is not used at all throughout the course.
Explanations about the presentations and reports:
3
, 4
Summary 2021
David Schouten
0. Course Introduction 2
1. Introduction to Responsible Data Science 5
2. The Data Dimension 9
3. The Algorithm Dimension 14
4. The Human Dimension 16
5. The Design Dimension Data visualisation 20
6. The Design Dimension: Explainable AI 24
6. The Design Dimension: Explainable AI Part 2 27
Exam information 31
1
,0. Course Introduction
26 April 2021
The course is taught by Evanthia Dimara, Jens Gulden and Anna Wegmann
The course is about a very multidisciplinary topic: designs aspects, engineering
aspects, psychological aspects, etc. So the best way to cover it is to circle around it
and cover all facets. It does this by taking on a high level scope, with less focus on
“technical” data science.
Data science is about accuracy and efficiency: what can we do with data.
Responsible data science is about what we should and shouldn’t do with data.
Deliverables and deadlines:
2
,The workshops are meant for prototyping, literature review, algorithms, writing and
critical thinking. They are mainly led by Anna and she also manages the Teams
channels.
Everything regarding the course is posted on MS Teams: Lectures, communication,
class materials, announcements, grades. Lectures are not recorded because a lot of
critical topics are discussed during the lectures: racism, discrimination, ethics. These
can bring a lot of discussions and everyone should feel confident to share their views
and thoughts. Blackboard is not used at all throughout the course.
Explanations about the presentations and reports:
3
, 4
