Universität Wien

053620 VU Data Ethics and Legal Issues (2021S)

Prüfungsimmanente Lehrveranstaltung


Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").


max. 25 Teilnehmer*innen
Sprache: Englisch


Termine (iCal) - nächster Termin ist mit N markiert

Montag 08.03. 15:00 - 18:00 Digital
Dienstag 09.03. 15:00 - 18:00 Digital
Mittwoch 10.03. 15:00 - 18:00 Digital
Donnerstag 11.03. 15:00 - 18:00 Digital
Donnerstag 25.03. 15:00 - 18:00 Digital
Freitag 26.03. 15:00 - 18:00 Digital
Donnerstag 22.04. 15:00 - 18:00 Digital
Freitag 23.04. 15:00 - 18:00 Digital
Mittwoch 05.05. 15:00 - 18:00 Digital
Mittwoch 12.05. 15:00 - 18:00 Digital
Mittwoch 19.05. 15:00 - 18:00 Digital
Mittwoch 26.05. 15:00 - 18:00 Digital
Mittwoch 02.06. 15:00 - 18:00 Digital
Mittwoch 09.06. 15:00 - 18:00 Digital
Mittwoch 16.06. 15:00 - 18:00 Digital
Mittwoch 23.06. 15:00 - 18:00 Digital
Mittwoch 30.06. 15:00 - 18:00 Digital


Ziele, Inhalte und Methode der Lehrveranstaltung

Students will be introduced to ethical and legal challenges when dealing with real data. Specifically, the topics of the course are structured into four parts, two on ethical issues and two on legal issues.

The first part will cover the following ethical issues by means of lectures with discussion:
* Introduction to ethics of AI & data science + narratives about AI
* Privacy and digital labor + future of work
* Responsibility and explainability + Bias/fairness
* Climate and environment: Opportunities and ethical problems

The second part will bridge to the more practical/empirical and political-social aspects and include the following topics:
* Critical Data and Algorithm Studies, how to reflect data practices, abrief introduction to Science and Technology Studies (STS)
* Everyday surveillance, human sensors
* Hands-on project: experimenting with data / ML: Training ML, data sets, open data (for DH Students, we can tailor this to specific interests)
* Presentation of project findings and discussion

The third part will cover legal issues on:
* Introduction into the legal system in Europe and Austria / legal resources
* Introduction to European data protection and data security law
IP, in particular copyright, licenses
* Recent trends, in particular digital services act

In the fourth part, we will be building on the introduction to legal basics outlined above. The course will provide a detailed overview of the most commonly encountered legal issues in DH projects.
* Example case studies - legal issues with source material:
- Copyright on primary texts
- Copyright on images (works of art)
- Data privacy issues with photographs
- Data privacy issues with diaries & letters
- Orphan works
* Example case studies - legal issues with research data:
- Ownership of scans
- Ownership of raw data; ownership of processed data
- Copyright on (scholarly) editions
- Ownership of scans
- Ownership of research output (e.g. papers)
- Ownership of code
- Research data about living persons and data privacy
- Non-research data about living persons and data privacy

In addition, the course will introduce a number of tools developed and infrastructure maintained by the DH community to tackle these issues (e.g. License Selector, Consent Form Wizard). Students will learn about the most important research infrastructures in the field of DH (CLARIN, DARIAH) and their working groups on legal and ethical issues (CLIC, ELDAH). Additionally, the relevance of the legal framework in which we conduct our research and its consequences for the implementation of Open Science approaches will be discussed.

Art der Leistungskontrolle und erlaubte Hilfsmittel

20% essay
30% midterm
20% presentations
30% final

Mindestanforderungen und Beurteilungsmaßstab

There is no mandatory prerequisite for this class.

The grading scale for the course will be:
1: at least 87.5%
2: at least 75.0%
3: at least 62.5%
4: at least 50.0%

In order to pass the course successfully, you will need to reach a minimum of 30% on each of the four assessments (midterm, essay, presentation, final).


* Ethical issues raised by AI and data science
* Societal challenges
* Legal Basics
* Data protection and intellectual property law
* Current legal developments
* DH tools for legal issues in practice
* DH research infrastructures
* Open Science
* Legal issues with source material
* Legal issues with research data


* Coeckelbergh, Mark. 2020. AI Ethics. MIT Press.
* Coeckelbergh, Mark. 2019. Artificial Intelligence, Responsibility Attribution, and a Relational Justification of Explainability. Science and Engineering Ethics, https://link.springer.com/article/10.1007/s11948-019-00146-8
* Fuchs, Christian & Sevignani 2013 What is Digital Labour? https://www.triple-c.at/index.php/tripleC/article/view/461
* House of Commons 2018 report “Algorithms in Decision-Making https://publications.parliament.uk/pa/cm201719/cmselect/cmsctech/351/35104.htm
* Mittelstadt, Brent, et al. 2016. The ethics of algorithms: Mapping the Debate. Big Data & Society https://journals.sagepub.com/doi/full/10.1177/2053951716679679
* Zou, James & Schibinger, Londa. AI can be sexist and racist - it’s time to make it fair. Nature https://www.nature.com/articles/d41586-018-05707-8
* OANA: Vienna Principles. A Vision for Scholarly Communication, 2015/16. https://viennaprinciples.org/
* Wilkinson, Mark D.; Dumontier, Michel; Aalbersberg, IJsbrand Jan; Appleton, Gabrielle; et al. (15 March 2016). "The FAIR Guiding Principles for scientific data management and stewardship". Scientific Data 3: 160018. doi:10.1038/sdata.2016.18. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4792175/
* Open Science Network Austria OANA. https://www.oana.at/
* DARIAH-EU. https://www.dariah.eu/
* DARIAH working group Ethics & Legality in Digital Arts & Humanities ELDAH. https://eldah.hypotheses.org/
* CLARIN ERIC. https://www.clarin.eu/
* CLARIN Legal and Ethical Issues Committee CLIC: Copyright Law Overview. https://www.clarin.eu/content/clic-overview-copyright-law
* CLARIN Legal and Ethical Issues Committee CLIC: Introduction to Copyright and Related Rights. Orphan works. https://www.clarin.eu/content/clic-orphan-works
* Vanessa Hannesschläger. Common Creativity international. CC-licensing and other options for TEI-based digital editions in an international context. In Journal of the Text Encoding Initiative, Issue 11 (2016 Conference Issue), July 2019 -, Online since 17 November 2019. DOI: https://doi.org/10.4000/jtei.2610
* Kamocki, Paweł, Pavel Stranák, and Michal Sedlák. “The Public License Selector: Making Open Licensing Easier.” Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016), Portorož, Slovenia, edited by Nicoletta Calzolari et al. Paris: European Language Resources Association (ELRA) 2016, 2533–2538. http://www.lrec-conf.org/proceedings/lrec2016/pdf/880_Paper.pdf
* DARIAH ELDAH Consent Form Wizard (CFW). https://consent.dariah.eu/
* Bates, Jo, Yu-Wei Lin, and Paula Goodale. 2016. ‘Data Journeys: Capturing the Socio-Material Constitution of Data Objects and Flows’. Big Data & Society 3(2):205395171665450. doi: 10.1177/2053951716654502.
* Ienca, Marcello, and Effy Vayena. 2020. ‘On the Responsible Use of Digital Data to Tackle the COVID-19 Pandemic’. Nature Medicine 26(4):463–64. doi: 10.1038/s41591-020-0832-5.
* Kitchin, Rob. 2014. ‘Big Data, New Epistemologies and Paradigm Shifts’. Big Data & Society 1(1):205395171452848. doi: 10.1177/2053951714528481.
* Olteanu, Alexandra, Carlos Castillo, Fernando Diaz, and Emre Kıcıman. 2019. ‘Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries’. Frontiers in Big Data 2:13. doi: 10.3389/fdata.2019.00013.
* European IPR Helpdesk, Copyright Essentials (2017). https://www.iprhelpdesk.eu/sites/default/files/newsdocuments/Fact-Sheet-copyright_essentials.pdf
* Kohl, U., & Charlesworth, A. (2016). Information Technology Law https://doi-org.uaccess.univie.ac.at/10.4324/9780203798522
* EU, Handbook on European data protection law (2018) https://op.europa.eu/en/publication-detail/-/publication/5b0cfa83-63f3-11e8-ab9c-01aa75ed71a1 (Sections 2, 3, 4, 6.1, 9.4, 10.1)

Zuordnung im Vorlesungsverzeichnis

Modul: DEL

Letzte Änderung: Fr 12.05.2023 00:13