The objective of this course is to provide an overview of the conditions for ethical and responsible data science, relevant to all ways of processing data - from the most traditional to the most advanced. The objective is twofold, to raise awareness of the ethical issues related to data processing, as well as to provide students with practical guidelines that can help them in their future work. Students will become familiar with the essential issues and perspectives, and acquire a reflective posture that helps them recognize potential problems, identify good practices, and locate existing infrastructures and devices that can help them (such as data archives, support services, and data use guidelines).
At the end of this course, you will:
- Assess the benefits and risks of a data collection and/or analysis project
- Identify the ethical issues that arise at each stage, particularly in a digital context
- Be aware of solutions (guidelines, support and advice services etc.)
Learning and teaching activities
We will meet for four one and a half hour sessions, during which the themes of the course will be discussed using concrete use cases, which illustrate their relevance and potential impact. You will be asked to reflect on the cases and to look for solutions yourself using the tools provided.
What is ethics? Why ethics for the data scientist?
The power of (data) science and technology
Benefits and costs of data
- Consent and information
- Legal frameworks
- Open data
- Solutions, help and resources
Guides, good practices, infrastructures and services
ACM (Association for Computing Machinery), 2018. Code of Ethics and Professional Conduct, https://www.acm.org/code-of-ethics
AOIR (Association of Internet Researchers), 2019. Internet Research: Ethical Guidelines 3.0, https://aoir.org/reports/ethics3.pdf
Buchanan E. & Zimmer M. 2016. Internet Research Ethics. The Stanford Encyclopedia of Philosophy, E.N. Zalta (ed.): https://plato.stanford.edu/entries/ethicsinternet-research/
INSHS, 2021. Les sciences humaines et sociales et la protection des données à caractère personnel dans le contexte de la science ouverte. Guide pour la recherche, Version 2, https://www.inshs.cnrs.fr/sites/institut_inshs/files/pdf/Guide_rgpd_2021.pdf
Tubaro P., Ryan L., Casilli A.A. & D'Angelo A. 2021. Social network analysis: New ethical approaches through collective reflexivity. Social Networks, 67: 1-8, https://doi.org/10.1016/j.socnet.2020.12.001
Zook M., Barocas S., Crawford K., Keller E., Goodman A., Hollander R., Koenig B.A., Metcalf J., Narayanan A., Nelson A. & Pasquale F. 2017. Ten simple rules for responsible big data research. PLOS Computational Biology, 13(3):1–11.