Universität Wien FIND

Due to the COVID-19 pandemic, changes to courses and exams may be necessary at short notice. Inform yourself about the current status on u:find and check your e-mails regularly.

Please read the information on https://studieren.univie.ac.at/en/info.

Warning! The directory is not yet complete and will be amended until the beginning of the term.

053620 VU Data Ethics and Legal Issues (2021S)

Continuous assessment of course work

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 25 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

Monday 08.03. 15:00 - 18:00 Digital
Tuesday 09.03. 15:00 - 18:00 Digital
Wednesday 10.03. 15:00 - 18:00 Digital
Thursday 11.03. 15:00 - 18:00 Digital
Thursday 25.03. 15:00 - 18:00 Digital
Friday 26.03. 15:00 - 18:00 Digital
Thursday 22.04. 15:00 - 18:00 Digital
Friday 23.04. 15:00 - 18:00 Digital
Wednesday 05.05. 15:00 - 18:00 Digital
Wednesday 12.05. 15:00 - 18:00 Digital
Wednesday 19.05. 15:00 - 18:00 Digital
Wednesday 26.05. 15:00 - 18:00 Digital
Wednesday 02.06. 15:00 - 18:00 Digital
Wednesday 09.06. 15:00 - 18:00 Digital
Wednesday 16.06. 15:00 - 18:00 Digital
Wednesday 23.06. 15:00 - 18:00 Digital
Wednesday 30.06. 15:00 - 18:00 Digital

Information

Aims, contents and method of the course

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.

Assessment and permitted materials

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

Minimum requirements and assessment criteria

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).

Examination topics

* 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

Reading list

* 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
* Austrian Centre for Digital Humanities & Cultural Heritage ACDH-CH: Legal Aspects of Digital Humanities Projects. https://www.oeaw.ac.at/acdh/services/legal-aspects-of-dh-projects/
* 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)

Association in the course directory

Modul: DEL

Last modified: Th 08.04.2021 08:27