Debates On Ethics of Data Science

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Credits
3
Types
Elective
Requirements
This subject has not requirements, but it has got previous capacities
Department
ESSI
Web
https://learnsql.fib.upc.es/moodle/
In this course we debate the impact on society of new advances in Data Science. We focus on ethics and the impact of Data Science on society. This course fosters the social competences of students, by building on their social responsibility, but also acquiring communication skills to debate about Data Science problems from an ethical perspective. The overall aim is to develop their critical attitude and reflection for social good.

Teachers

Person in charge

  • Oscar Romero Moral ( )

Others

  • Alberto Abello Gamazo ( )
  • Besim Bilalli ( )

Weekly hours

Theory
0.6
Problems
0.8
Laboratory
4
Guided learning
0
Autonomous learning
9.6

Competences

Transversal Competences

Sustainability and social commitment

  • CT2 - Capability to know and understand the complexity of economic and social typical phenomena of the welfare society; capability to relate welfare with globalization and sustainability; capability to use technique, technology, economics and sustainability in a balanced and compatible way.

Third language

  • CT5 - Achieving a level of spoken and written proficiency in a foreign language, preferably English, that meets the needs of the profession and the labour market.

Gender perspective

  • CT6 - An awareness and understanding of sexual and gender inequalities in society in relation to the field of the degree, and the incorporation of different needs and preferences due to sex and gender when designing solutions and solving problems.

Basic

  • CB7 - Ability to integrate knowledge and handle the complexity of making judgments based on information which, being incomplete or limited, includes considerations on social and ethical responsibilities linked to the application of their knowledge and judgments.

Technical Competences

Especifics

  • CE12 - Apply data science in multidisciplinary projects to solve problems in new or poorly explored domains from a data science perspective that are economically viable, socially acceptable, and in accordance with current legislation
  • CE13 - Identify the main threats related to ethics and data privacy in a data science project (both in terms of data management and analysis) and develop and implement appropriate measures to mitigate these threats

Objectives

  1. Acknowledge the current and future impact of next generation analytical systems on society
    Related competences: CT2, CT5, CT6, CE12, CE13, CB7,
  2. Ability to study and analyze problems in a critical mood
    Related competences: CT2, CT5, CT6, CE12, CE13, CB7,
  3. Ability to critically read texts
    Related competences: CT2, CT5, CT6, CE12, CE13, CB7,
  4. Develop critical reasoning with special focus on ethics and social impact
    Related competences: CT2, CT5, CT6, CE12, CE13, CB7,
  5. Develop soft skills to defend - criticize a predetermined position in public
    Related competences: CT2, CT5, CT6, CE12, CE13, CB7,
  6. Improve the writing skills
    Related competences: CT2, CT5, CT6, CE12, CE13, CB7,

Contents

  1. Introduction: Debate Rules and Course Structure
    In this first module we will present the course, its structure and methodology.
  2. Ethics and social impact of next generation analytical systems: Debates
    After presenting what will be next in the area of data science and big data, in this module we discuss the impact these new ideas will have on society. More specifically, we will discuss about ethics, personal data protection, hacking, licensing / patenting, IP rights, etc. The discussion will be on the form of debates.
  3. Read a book to develop your ethical reasoning
    A mandatory book read that will develop the ethical reasoning of the students

Activities

Activity Evaluation act


Introduction

The course is introduced. We will discuss the course structure, the methodology and the evaluation.
Objectives: 1
Contents:
Theory
3h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
8h

Debates on Ethics and social impact of next generation analytical systems and Big Data

During these sessions the debates discussing ethics and social impact of next generation analytical systems and Big Data will take place. You must read the available material before the debate. Then, during the debate you will assign to a group: either to defend an idea, or go against it. You may also be asked to moderate the debate. Then, the debate takes place and afterwards, each group needs to write down a report with their conclusions
Objectives: 1 2 3 4 5 6
Contents:
Theory
0h
Problems
0h
Laboratory
20h
Guided learning
0h
Autonomous learning
19h

Read a seminal book on ethics for data science and Big Data

Reads the book and conducts the assessment provided
Objectives: 1 2 3 4 5 6
Contents:
Theory
0h
Problems
4h
Laboratory
0h
Guided learning
0h
Autonomous learning
21h

Teaching methodology

There will be 6 face-to-face sessions. The first one introduces the course. The other will be debates. Before each debate, a proposed topic is given, together with some basic material (typically papers) to foste a debate during the next lecture.

The students are meant to read the material, and look for additional stuff, *before* the lecture so that they can better defend their position during the debate.
During the lecture, there will be an organized debate (pro and against groups will be configured as well as a moderator).
After the debate, each group (pro, against and moderator) will be asked to write down their debate conclusions.

The course methodology puts the focus on three main aspects:
- Critical reasoning (with special focus on ethics and social impact),
- Develop soft skills to defend - criticize a position in public,
- Improve the writing skills summarizing an event.

The course methodology wraps up with the read and reflection of a seminal book on ethics for data science.

Evaluation methodology

Each debate entails two main parts:
- (60%) The face-to-face debate Db (this mark is computed from the report written by the moderator group and supervised by the lecturers),
- (40%) The written conclusions Wr.

Thus, each debate mark (Di) is computed as Di = Db*0,6 + Wr*0,4. The final mark will be computed as the average of the debates. Those students not debating will have to write a report and their session mark will be 100% on Wr (i.e., Di = Wr).
The final evaluation of the debates (DM) is the average mark of the debates.

The book reading (BM) is evaluated by means of a deliverable related to it.

The course final mark is calculated as follows: 0,8*DM + 0,2*BM.

The evaluation is done on an individual basis.

Bibliography

Basic:

Previous capacities

Basic knowledge in Data Management and Analytics