Ethics in Data Science

Objectives

In this course we will introduce the ethical and social implications of the decisions we make at all stages of the data analysis pipeline, from data collection to storage to understanding feedback loops in analysis.

General characterization

Code

200292

Credits

3.5

Responsible teacher

João Miguel Valente Cordeiro

Hours

Weekly - Available soon

Total - Available soon

Teaching language

Portuguese. If there are Erasmus students, classes will be taught in English

Prerequisites

TBD

Bibliography

Teaching method

Teaching methodologies will be adapted to the nature of the different Curricular Unit (CU) subjects and includes lectures, critical analysis, and discussion of practical cases. Each session will introduce new concepts and methodologies, as well as the applications of the learnt concepts using different ethical discourses. Different learning strategies will be used, such as lectures, slide show demonstrations, step-by-step tutorials on how to approach practical examples, questions, and answers.

The practical component is focused in exploring ethical dilemmas, including a discussion on the best approach under different scenarios.

Evaluation method

- Final written exam (100%).

Subject matter

  • Foundations of Ethics (the role of Ethics; Ethics and Science; Ethics and the Law)
  • Tools for ethical decision making
  • Normative frameworks applicable to Data Science
  • Data Science Ethics – paradigm cases
  • Privacy, confidentiality, and data protection: the role of ethics
  • Privacy, confidentiality and data protection – legal reasoning
  • Artificial Intelligence and Data Science – ethical, legal and social issues
  • Cybersecurity and methods to ensure data privacy
  • Ethics Design Sprint (the intersection between behavioural science and data science ethics)