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/
This course explores and critically debates the societal impact of recent advances in Data Science. It focuses on the ethical challenges arising from the development and deployment of data-driven technologies, examining their implications for individuals, communities, and institutions.

The course fosters students¿ social and ethical competences by strengthening their sense of social responsibility and enhancing their ability to communicate and argue effectively about complex Data Science issues from an ethical perspective. Through structured debates and critical analysis, students develop the capacity to assess technological developments not only from a technical standpoint, but also in terms of fairness, accountability, transparency, and social good.

The overall aim of the course is to cultivate critical thinking, ethical reflection, and a responsible professional attitude toward the societal role of Data Science.

Teachers

Person in charge

  • Oscar Romero Moral ( )

Others

  • Petar Jovanovic ( )

Weekly hours

Theory
0.9
Problems
0
Laboratory
3
Guided learning
0
Autonomous learning
6.85

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 data-driven systems: Debates
    This course is structured around formal debates inspired by debate leagues.

    Students argue both sides of ethical dilemmas related to Data Science and data-driven technologies. Each debate explores the societal, legal, and moral implications of real-world data practices. Participants develop evidence-based arguments, rebuttals, and critical questioning skills. The format strengthens ethical reasoning, communication abilities, and responsible professional judgment.
  3. Applied ethical evaluation in Data Science
    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

Ethics and social impact of next generation data-driven systems: Debates

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
2h
Problems
0h
Laboratory
15h
Guided learning
0h
Autonomous learning
15h

Applied Ethical Evaluation on Data Science

Conducts a real project and presents results
Objectives: 1 2 3 4 5 6
Contents:
Theory
1h
Problems
0h
Laboratory
6h
Guided learning
0h
Autonomous learning
25h

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 ta practical project.

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 project (P) is evaluated by means of a deliverable related to it.

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

Bibliography

Basic:

Previous capacities

Basic knowledge in data management and analysis