Universität Wien

053620 VU Data Ethics and Legal Issues (2024S)

6.00 ECTS (4.00 SWS), SPL 5 - Informatik und Wirtschaftsinformatik
Continuous assessment of course work
Moodle

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

***Online exam (covering contents of session 5-10 on legal issues) on 3 JUN 2024.***

  • Monday 04.03. 13:15 - 16:30 Digital
  • Monday 11.03. 13:15 - 16:30 Hörsaal 34 Hauptgebäude, Hochparterre, Stiege 6
  • Monday 18.03. 13:15 - 16:30 Hörsaal 34 Hauptgebäude, Hochparterre, Stiege 6
  • Monday 08.04. 13:15 - 16:30 Hörsaal 34 Hauptgebäude, Hochparterre, Stiege 6
  • Monday 15.04. 13:15 - 16:30 Digital
  • Monday 22.04. 13:15 - 16:30 Digital
  • Monday 29.04. 13:15 - 16:30 Digital
  • Monday 06.05. 13:15 - 16:30 Digital
  • Monday 13.05. 13:15 - 16:30 Digital
  • Monday 27.05. 13:15 - 16:30 Digital
  • Monday 03.06. 13:15 - 16:30 Digital
  • Monday 10.06. 13:15 - 16:30 Hörsaal 34 Hauptgebäude, Hochparterre, Stiege 6
  • Monday 17.06. 13:15 - 16:30 Hörsaal 34 Hauptgebäude, Hochparterre, Stiege 6
  • Monday 24.06. 13:15 - 16:30 Hörsaal 21 Hauptgebäude, Hochparterre, Stiege 8

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. Both topics will be discussed on a principle level as well as from a practical perspective.

The first part will cover a first introduction to ethical issues around data and technology 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, a brief 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
Assessment of the first part will be by active participation in the discussion and the group projects.

The second part of the course introduces students to legal thinking and will cover the following scope of legal issues in the digital realm:
* Introduction into the legal system in Europe and Austria / legal resources
* Introduction to European data protection and data security law
*Introduction to intellectual property law, in particular copyright, licenses, and text and data mining
* Recent trends, in particular Digital Services Act and Artificial Intelligence Act

The third part of the course moves into practical questions and 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 around technological and data related aspects in digital humanities / research projects. This includes
Source material and copyright (copyright, licensing and Open Science, orphan works and more)
data privacy of research subjects (GDPR, living research subjects, deceased research subjects, other invovled persons)
In addition, the course will introduce a number of tools developed and infrastructure maintained by the DH community to tackle these issues. 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).
Both parts of the course relating to legal issues will be assessed by an exam.

The final part of the course 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, a brief 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

Assessment and permitted materials

50% participation, group work & presentations
50% exam

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 assessments.

Examination topics

* Ethical issues raised by AI and data science
* Societal challenges
* Legal Basics
* Data protection and intellectual property law
* Current legal developments, especially concerning the regulation of artificial intelligence
* 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
* Crawford, Kate. 2021. Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. New Haven: Yale University Press.
* Perez, Caroline Criado. 2019. Invisible Women: Data Bias in a World Designed for Men. New York: Abrams.
* Dignum, Virginia. 2020. “Responsibility and Artificial Intelligence.” In The Oxford Handbook of Ethics of AI, edited by Marcus Dubber, Frank Pasquale, and Sunit Das, 215–33. University of Oxford Press.
* 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
* 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 (2022). https://op.europa.eu/en/publication-detail/-/publication/8ca54353-87f9-11ec-8c40-01aa75ed71a1/language-en/format-PDF/source-251139731
* Share Your Work, Creative Commons. https://creativecommons.org/share-your-work/
* 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: Sa 24.02.2024 20:25