Academic Ethics and Integrity: Methodology of Scientific Research

1st year
Programme main editor:
Onsite in:
ECTS range:



Pedagogical objectives:

The course aims to present various ethical aspects in computer science and artificial intelligence, intellectual property issues and the methodology of scientific research.

Evaluation modalities:

The evaluation consists of lab reports and/or research presentations.


The students will gain an understanding of the different regulations, laws, and practical aspects of academic ethics and integrity, pertaining to research and development in computer science.

Topics :

  • Introduction. Responsibility, ethics and integrity of the software developer in the creation of a software product.
  • Scientific research, grants and projects.
  • Scientific articles: creation, structure, evaluation, acceptance criteria, presentation.
  • Evaluation of research, academic rankings.
  • Research funding, resources, conflicts of interest.
  • Ethical aspects of research, ethics in multidisciplinary research.
  • Plagiarism and intellectual property.
  • Ethics in the digital era.

Required teaching material

• ACM/IEEE-Computer Society. Software Engineering Code of Ethics and Professional Practice. Version 5.2. • Council for Big Data, Ethics & Society. • Herschel, Richard and Miori, Virginia (2017) “Ethics & Big Data,” Technology in Society 49, 31- 36. • Buchanan, Elizabeth and Zimmer, Michael (2016) “Internet Research Ethics,” The Stanford Encyclopedia of Philosophy, Edward N. Zalta (ed.), internet-research/ • Floridi, Luciano, and Taddeo, Mariarosaria (2016) “What is Data Ethics?” Philosophical Transactions ofthe Royal Society A, 374:2083, DOI: 10.1098/rsta.2016.0360. In special issue with the theme The Ethical Impact of Data Science, Taddeo and Floridi eds. • Metcalf, Jason and Crawford, Kate (2016) “Where are Human Subjects in Big Data Research? The Emerging Ethics Divide,” Big Data & Society 3:1, DOI: 10.1177/2053951716650211 • O’Leary, Daniel E. (2016) “Ethics for Big Data and Analytics,” IEEE Intelligent Systems, 31:4, 81- 84. • Crawford, Kate, et al. (2014) “Critiquing Big Data: Politics, Ethics, Epistemology.” International Journal of Communication, 8:1663-1672. • Richards, Neil M. and King, Jonathan H. (2014) “Big Data Ethics,” Wake Forest Law Review. Available at SSRN: • Zwitter, Andrej (2014) “Big Data Ethics,” Big Data & Society, Jul-Dec, 1-6. • Moreno, M.A., et al. (2013) “Ethics of Social Media Research: Common Concerns and Practical Considerations.” Cyberpsychol Behav Soc Netw. 16(9):708-13. doi: 10.1089/cyber.2012.0334.

Teaching volume:
28 hours
Supervised lab:
28 hours


  • Laboratory-Based Course Structure
  • Open-Source Software Requirements